AIMD Paper Review

AIMD Summary

1987年美国堪萨斯大学物理与天文学系R.A. Murray等人000计算了硅酸钠玻璃(Na2O)x(SiO2)1−x 的电子结构，包含该系统的态密度、分波态密度、单电子态的局域化和有效价电荷。论文发表在Journal of Non-Crystalline Solids期刊上。该论文是目前已找到的最早计算硅酸盐体系电子结构的论文。

1999年J. C. G. Pereirapart1,part2基于从头算开始研究硅酸盐体系中的复杂团簇。

2000年英国伯贝克学院和伦敦大学学院的D. Alfè团队000基于从头算分子动力学模拟研究了溶解在固体和液体铁中的轻元素的化学势，论文发表在Nature期刊上。
2002年英国伦敦大学学院物理与天文学系的M.J Gillan团队000基于ab initio 技术计算地球固体内核和液体外核中主要候选杂质元素（S、O 和 Si）的化学势。论文发表在Earth and Planetary Science Letters期刊上。
2003年日本新日本制铁公司的K.Iwata等人000提出了利用基于密度泛函理论的第一性原理计算来预测无限稀Si溶液中溶质元素的活度系数和相互作用参数的方法。论文以题目Prediction of thermodynamic properties of solute elements in Si solutions using first-principles calculations发表在Acta Materialia期刊上。该团队2012年在MMTB上发表了类似论文。

2004年英国贝尔法斯特女王大学的Anthony T. Paxton团队000基于vasp研究了微量杂质Bi引发的铜脆化机理，论文发表在Nature上。
2004年美国劳伦斯利弗莫尔国家实验室Christopher J. Mundy团队000基于CPMD进行了水液-汽界面的从头算分子动力学研究，论文发表在Science期刊上。

2005年英国伦敦大学学院的Dario Alfè团队000基于第一性原理计算绘制了 MgO 熔化曲线，论文发表在PHYSICAL REVIEW LETTERS期刊上。

2006年英国伦敦大学学院的Antonio Tilocca团队000基于DL_POLY和Quantum-ESPRESSO进行45S5 生物活性硅酸盐玻璃的 Ab Initio 分子动力学研究，利用 CPMD 模拟的准确性来深入了解短程结构并分析该生物材料的振动和电子特性。该作者研究方向为生物活性玻璃从头算研究，注意关注该作者文章。 该作者的模拟体系为SiO2-Na2O-CaO-P2O5，模拟的原子数为，
The final configuration was then used as a starting point for the CPMD simulation. CPMD runs at constant volume were carried out with use of the CP code included in the Quantum-ESPRESSO package
2006年英国伦敦大学学院的S. Ostanin团队000基于从头算研究了氩在高压和高温下在铁水中的溶解度。论文发表在Geophysical research letters期刊上。
2006年美国加州大学的Yong Jiang团队000提出了一种从第一原理预测稀二元固溶体中活度系数的方法，并将其应用于镍中的铝。论文发表在Scripta Materialia期刊上。题目为Activity coefficients for dilute solid solutions of Al in Ni。

2009年中国科学院地质与地球物理研究所岩石圈演化国家重点实验室的Yigang Zhang团队000基于vasp进行了两相从头算分子动力学模拟，研究了Si和O在液态铁和硅酸盐熔体之间的分配，论文发表在Geophysical Research Letters.
2009年Rodolphe Vuilleumier等人000基于CPMD进行天然硅酸盐熔体的计算机建模：我们可以从ab initio模拟中学到什么，论文发表在Geochimica et Cosmochimica Acta期刊上。
2009年英国伦敦纳米技术中心的Dario Alfè000基于两相从头算分子模拟研究了固液界面温度，即地球内核边界的温度：来自第一性原理共存模拟的高压铁熔化，论文发表在PHYSICAL REVIEW B期刊上。

2010年吉林大学的Dong Han等人000基于vasp研究了二氧化硅的取代掺杂特性。掺杂剂包括来自第 III 族、第 V 族和第 VII 族的一系列元素。论文发表在PHYSICAL REVIEW B期刊上。

2011年Alfonso Pedone团队000基于Quantum-ESPRESSO package软件对SiO2-Na2O-P2O5-CaF2-CaO生物玻璃体系进行从头算分子动力学模拟，论文发表在JPCB上。
2011年英国伦敦大学的Antonio Tilocca团队000进行了生物活性玻璃中的氟环境的ab Initio分子动力学模拟研究，论文发表在JPCB期刊上。

2012年意大利的Alfonso Pedone团队000基于第一性原理计算和固态核磁共振光谱进行了含氟生物活性玻璃结构的研究，论文发表在Journal of Materials Chemistry期刊上。
2012年中国科学院地质与地球物理研究所地球深部重点实验室的Yigang Zhang团队000基于vasp进行了两相共存第一性原理分子动力学 (MD)模拟，以研究碳和其他轻元素在金属和硅酸盐熔体之间的分配。论文发表在PNAS期刊上。
2012年日本新日铁的Tooru Matsumiya000基于vasp进行了硅熔体中溶质的活度系数和相互作用参数的估计，论文发表在MMTB上。这是一篇很重要的论文，因为这篇论文中通过vasp计算出了一些无法手工计算的统计热力学参量，并将其应用到活度的计算中。论文以题目Estimation of Activity Coefficients and Interaction Parameters of Solutes in Silicon Melts发表在MMTB上。

2013年德国地球科学研究中心Volker Haigis团队000基于cp2k的QS模块进行了硅酸盐熔体中Y的分子动力学模拟及其对微量元素分配影响的研究，论文使用基于经典MD的方法直接计算CaO-Al2O3-SiO2系统中熔体对之间的微量元素分配，论文发表在Chemical Geology期刊上。
2013年中国科学技术大学地球与空间科学学院，中国科学院地幔材料与环境重点实验室的Fang Huang团队000基于Quantum Espresso进行了石榴石、斜辉石、斜方辉石和橄榄石之间平衡镁同位素分馏的第一性原理计算：对镁同位素温度测量的影响。论文发表在Earth and Planetary Science Letters期刊上。
2013年德国GFZ 德国地球科学研究中心的Piotr M. Kowalski团队000基于CPMD软件包进行高温和高压下矿物和含水流体之间平衡硼同位素分馏的从头算预测，论文发表在Geochimica et Cosmochimica Acta期刊上。
2013年西班牙马德里材料科学研究所E.R. Hernández团队000基于vasp进行了将水掺入下地幔钙钛矿：第一性原理研究，论文发表在Earth and Planetary Science Letters期刊上。
2013年伦敦大学学院的Devis Di Tommaso团队000基于DL_POLY和Quantum-ESPRESSO 软件包 4.0.1 版中包含的 Car-Parrinello代码模拟三元P2O5-CaO-Na2O磷酸盐玻璃从熔体到固体非晶材料的结构演变，论文发表在Journal of Materials Chemistry B期刊上。
2013年韩国科学技术高等研究院的Geun-Myeong Kim等人000基于vasp研究了界面键合和缺陷对Si/SiO2界面硼扩散的影响，论文发表在Journal of Applied Physics期刊上。

2014年上海交通大学的孙宝德团队019基于vasp进行了TiB2(0 0 0 1)表面与Al熔体固液界面电子结构的从头算研究，论文发表在
Journal of Alloys and Compounds期刊上。
2014年美国威斯康星大学麦迪逊分校D. Morgan团队000基于lammps和vasp计算了含Cr溶质的熔融氟化物盐，论文发表在Journal of Nuclear Materials期刊上。
2014年美国的Dane Morgan团队008基于lammps和VASP计算了LiCl-KCl熔盐体系，论文发表在Computational Materials Science期刊上。
2014年中国科学技术大学地球与空间科学学院的黄方团队000基于Quantum espresso软件进行地幔矿物中平衡硅同位素分馏的第一性原理计算研究，论文发表在Geochimica et Cosmochimica Acta期刊上。
2014年日本同步辐射研究所的Shinji Kohara团队000基于高能 X 射线衍射测量和基于cp2k的DF-MD 模拟研究了极易碎液体的原子和电子结构，论文发表在nature communication上。初始原子配置是通过反向蒙特卡罗 (RMC) 模拟创建的，该模拟具有 18.98 Å 立方体中 501 个原子的高能 X 射线衍射数据（实验密度，每 Å3 为 0.0733 个原子）。使用了 RMC++ 程序代码。 Born-Oppenheimer MD 模拟是使用 Nóse-Hoover 恒温器进行的，时间步长为 2 fs（初始化）和 1 fs（数据收集）。 该系统在 3,100 K（—2,800 °C）下模拟，总共 30 ps，其中最后 10 ps 用于数据收集。相应的原子均方位移清楚地显示出液体行为（扩散）。
2014年伦敦大学的Jamieson K.Christie团队000基于cp2k的QS模块进行与在生物活性磷酸盐玻璃中掺入氟相关的结构变化的从头算分子动力学模拟，论文发表在Biomaterials。 MD 运行从 NVT 系综中的初始准随机配置在2500K开始，直到模型很好地平衡，这通过计算实际和均方原子位移得到证实。这通常需要 20 ps 的 MD 模拟时间。然后，每个模型在 NVT 集合中在以下每个温度下运行 10 ps：2200 K、1900 K、1600 K、1300 K、1000 K、750 K 和 500 K，然后在 NVT 集合中运行 20-25 ps NVT 集合在 300 K。

2015年上海应用物理研究所Jianxing Dai000等人基于cp2k进行了纯熔融ThF4和ThF4–LiF–BeF2熔体结构的分子动力学研究。
2015年美国威斯康星大学-麦迪逊分校Dane Morgan团队000基于lammps和vasp进行了“熔盐中的氧化还原条件和溶质行为：第一性原理分子动力学研究”，论文发表在Journal of Nuclear Materials期刊上。
2015年法国的Romain Dupuis团队000基于Quantum-Espresso包进行了液体中硅同位素的分馏：构型无序的重要性研究，论文发表在Chemical Geology期刊上。

2016年中南大学的Xiaojun Lv团队000基于CASTEP进行Na3AlF6熔盐的第一性原理分子动力学研究，论文发表在
Journal of Fluorine Chemistry期刊上。
2016年中南大学的Xiaojun Lv团队000基于DL_POLY和CASTEP进行Na3AlF6–Al2O3熔盐结构和输运特性的分子动力学研究，论文发表在Journal of Molecular Liquids期刊上。
2016年上海应用物理研究所Jianxing Dai000基于cp2k进行了LiF-BeF2熔盐中局部结构的结构和振动光谱的第一性原理研究，论文发表在Journal of Molecular Liquids期刊上。
2016年美国密苏里大学Wai-Yim Ching团队团队000基于vasp研究了无定形沸石咪唑酯骨架 (a-ZIF) 的连续随机网络模型的结构和电子特性，论文发表在JPCC上。
2016年英国伦敦大学的Konstantinos Konstantinou团队000基于cp2k进行(SiO2)57.5–(B2O3)10–(Na2O)15–(CaO)15–(MoO3)2.5和(SiO2) 57.3–(B2O3)20–(Na2O)6.8–(Li2O)13.4–(MoO3)2.5体系的核废料玻璃中钼的局部原子结构从头算分子动力学模拟研究，论文发表在PCCP上。
2016年德国地球科学研究中心的Georg Spiekermann团队000基于CPMD进行从头分子动力学研究超临界 H2O-SiO2流体的结构和动力学特性，论文发表在Chemical Geology。 Si-O键自相关函数显示平均 Si-O 键寿命在3000K 时为 26ps， 在2400K时飙升至200 ps。体系大小分别为 192 原子和 96 原子。
2016年美国西北太平洋国家实验室的Wooyong Um团队000基于cp2k进行了玻璃废料中放射性锝的AIMD，锝固定化对于放射性废物管理和环境修复至关重要，论文发表在nature communications期刊上。
2016年日本东北大学的Akihiko Hirata团队000基于cp2k研究了非晶一氧化硅的结构，论文发表在nature communication
2016年澳大利亚墨尔本大学的Maoyuan Liu000基于中子散射和密度泛函(vasp)进行了从原子结构到过剩熵：CaO−Al2O3−SiO2熔体的中子衍射和密度泛函理论研究，论文发表在Journal of Physics: Condensed Matter期刊上。

2017年英国伦敦大学的Konstantinos Konstantinou 014在其博士学位论文中基于cp2k等软件进行了多组分硅酸盐玻璃结构、动力学和电子特性的模拟计算研究。
2017年湖南大学Huiqiu Deng团队000基于CP2K中的Fist(分子动力学模拟)模块计算了LiF-BeF2-ThF4熔盐输运特性，并未涉及到量子化学或第一性原理计算，论文发表在Journal of Molecular Liquids期刊上。
2017年上海大学的Xuejiao Li000基于vasp模拟熔融LiCl-KCl共晶U3+配位结构的动态波动，论文发表在JPCA上。
2017年上海大学的Liuming Yan团队000基于lammps和vasp模拟了LiCl-KCl共晶中UCl3，论文发表在Journal of Molecular Liquids期刊上。
2017年美国的Jincheng Du团队000基于vasp计算了晶化锂硅酸盐玻璃的结构和电子性质，论文发表在JPCB上。
2017年美国密苏里大学Wai-Yim Ching团队000基于vasp计算了单一和混合碱硅酸盐玻璃结构和性能，论文发表在JPCA上。
2017年德国地球科学研究中心的Sandro Jahn团队000基于CP2K的QS模块研究了硅酸盐熔体之间的微量元素分配，论文发表在Geochimica et Cosmochimica Acta期刊上。
2017年德国地球科学研究中心的Sandro Jahn团队000基于cp2k的QS模块金属和硅酸盐熔体之间的镍分配，论文发表在Chemical Geology期刊上。
2017年中科大Fang Huang团队000基于Quantum Espresso进行浓度对碳酸盐矿物中镁钙同位素平衡分馏的影响：第一性原理计算的启示研究，论文发表在Geochimica et Cosmochimica Acta期刊上。
2017年英国伦敦大学的J. K. Christie团队000基于GULP软件拟合Buckingham势，DLPOLY代码进行分子模拟，CP2K进行第一性原理计算研究锶夹杂物对磷酸盐基玻璃生物活性的影响。论文并未说明所采用的具体基组和泛函。 The models were then run in an NVT ensemble at 2500 K for 20 ps, before being cooled in a series of runs in NVT ensembles of length 10 ps, decreasing the temperature by about 300 K in each run, corresponding to a cooling rate of ∼30 K/ps. The models were then run in an NVT ensemble at 300 K for ∼40–50 ps, the final two-thirds of which formed the production run.
2017年美国俄亥俄州立大学的Jinsuo Zhang团队000基于vasp进行液态钠中镧系元素的从头算分子动力学研究，论文发表在Journal of Nuclear Materials期刊上。
2017年哈尔滨工业大学的Yuelei Bai团队000基于CASTEP进行了三元层状硼化物 MoAlB 的密度泛函理论见解研究，论文计算了DOS，论文发表在Acta Materialia期刊上。
2017年美国加利福尼亚州伯克利市劳伦斯伯克利国家实验室的Lin-Wang Wang团队000基于从头算研究了c-Si/a-SiO 2界面原子结构对其能带排列的影响，论文发表在PCCP期刊上。

2018年中南大学的Xiaojun Lv团队010基于IPMD和FPMD研究了LiF-NaF-AlF3熔盐离子结构和输运性质，使用的软件为Dl_poly和CASTEP，论文发表在Chemical Physics Letters上。
2018年上海应用物理研究所Jian-Xing Dai009基于cp2k研究了LiCl-KCl熔盐中铀离子局域结构和输运性质，该论文发表在Journal of Nuclear Materials期刊上。
2018年澳大利亚卧龙岗大学Hongtao Zhu团队000基于vasp研究了硅酸钠在铁表面高温吸附解聚机理的第一性原理研究，论文发表在JPCC期刊上。
2018年法国的Marc Blanchard团队000基于Quantum ESPRESSO进行了锌与矿物之间的平衡同位素分馏第一性原理计算，论文发表在Chemical Geology期刊上。
2018年法国的Etienne Balan团队000基于QUANTUM ESPRESSO软件进行碳酸盐中结构硼与含水硼酸和硼酸盐离子之间的理论同位素分馏研究，论文发表在Geochimica et Cosmochimica Acta期刊上。
2018年英国帝国理工学院的Nick Quirke团队000基于cp2k在 B3LYP/6-31G** 理论水平上使用了从头分子动力学和密度泛函理论 (DFT) 计算来评估聚合物纳米复合材料许多代表性界面处多余电子的能量和定位。这些建模界面是通过将液态水与聚乙烯和二氧化硅的无定形板组合而成的。论文发表在PCCP上。
2018年华盛顿州立大学的S. Banerjee团队000基于cp2k的QS模块进行了Na2S+SiS2玻璃电解质中结构和Na+离子传输的分子动力学建模，论文发表在JPCB上。熔体淬火技术包括将系统初始加热到3000K的高温，并将熔体在此温度下保持100fs以随机化熔体。然后以10^14K/s的非常高的淬火速率将熔体淬火至室温。淬火玻璃在室温下在NVT（恒定原子数、体积和温度）集合下平衡10ps，然后生产运行30ps。对于生产运行，平衡期间的时间步长分别为0.1和1fs。
2018年华东理工大学的Yun Ding000基于cp2k的QS模进行稀释浓度NaCl溶液的从头算分子动力学研究，详细研究了结构、动力学和电子特性。论文发表在Computational and Theoretical Chemistry期刊上。
2018年北京科技大学先进金属与材料国家重点实验室Laiqi Zhang团队000基于vasp进行液态锌的动力学和热力学性质：从头分子动力学研究，论文发表在Computational Materials Science。

2019年美国威斯康星大学的Jianqi Xi团队000基于lammps和vasp研究熔盐中Si, C, 和 SiC 的腐蚀。
2019年中南大学的Hongliang Zhang团队000基于CASTEP模块进行KF-NaF-AlF3熔盐体系的第一性原理分子动力学研究，论文发表在Chemical Physics Letters期刊上。
2019年中南大学的Hongliang Zhang团队000基于CASPTEP模块进行Na3AlF6熔盐离子结构和电子性质的第一性原理分子动力学模拟，论文发表在Chemical Physics Letters期刊上。
2019年美国加利福尼亚大学De-en Jiang团队011基于vasp研究了UCln–NaCl(n=3,4)熔盐体系，论文发表在ACS Applied Energy Materials。
2019年中南大学的Xiaojun Lv团队007基于IPMD和FPMD研究了KF-NaF-AlF3熔盐的离子结构和传输特性，使用的软件为Dl_poly和CASTEP，论文发表在PCCP上。
2019年美国的Fei Wu012基于AIMD阐明熔融MgCl2–KCl混合物中简单电荷交替之外的离子相关性，论文发表在The Journal of Physical Chemistry Letters。
2019年Jicheng Guo000基于vasp研究了熔融LiCl-Li溶液的流体结构，论文发表在JPCB上。
2019年美国密苏里大学Wai-Yim Ching团队000基于vasp研究了新型仿生水泥中硅铝石晶体与二氧化硅结合肽的界面相互作用，论文发表在ACS Combinatorial Science上。
2019年上海应用物理研究所的Jianxing Dai团队000基于cp2k的Fist模块进行了熔融AF-ThF4体系（A+ = Li+、Na+和K+）的微观结构和宏观热物理性质的理论评估研究，论文发表在Journal of Molecular Liquids期刊上。
2019年美国俄亥俄州立大学Dale Igram团队000基于vasp研究了三元玻璃状硫属化物材料 Ag20Ge28Se52的结构、电子、振动和离子动力学特性。论文发表在Journal of Non-Crystalline Solids期刊上。模型是通过在 2500 K 下形成平衡液体 12 ps，然后在 10 ps 内从 2500 K 淬火到 1500 K，然后在 1500 K 下平衡 6 ps，并在 12 ps 内从 1500 K 淬火到 300 K 来构建的并在 300 K 下平衡 20 ps。
2019年美国华盛顿州立大学的S. Banerjee团队000基于cp2k的QS模块通过从头分子动力学研究 Na2S + P2S5玻璃电解质的结构及其对 Na+ 离子电导率的影响。论文发表在Solid State Ionics期刊上。熔体淬火技术包括将系统初始加热到 1500K 形成熔体，并将熔体在此温度下保持 10 秒以随机化熔体。 然后，我们以 10^14K/s 的非常高的淬火速率将熔体淬火至室温。 我们最初在 NPT（恒定原子数、压力和温度）集合下在室温 (300K) 下平衡淬火玻璃 100ps，以达到平衡密度。 然后该系统在 NVT（恒定原子数、体积和温度）集合下在 300K 下进一步平衡 100ps，以去除淬火过程中的任何残余应力。 最后，我们通过分析系统在 NVT 集合下 40ps 的生产运行轨迹来分析所有结构和离子传输特性。
2019年俄罗斯叶卡捷琳堡高温电化学研究所Dmitry Zakiryanov团队000基于cp2k的QS模块进行熔融卤化铅 PbX2 (X = Cl, Br, I) 的局部结构和振动特性的从头算分子动力学模拟研究，论文发表在Computational and Theoretical Chemistry期刊上。For each system the calculation was carried out in the NVT ensemble at two temperatures: 800 and 900 K for lead chloride, 700 and 800 K for bromide and iodide. Temperatures were chosen to be near the melting points as well. In addition, in order to establish the influence of the system size on the results, we performed simulations for systems with 72 and 108 atoms (24 and 36 PbX2 formula units, respectively). The calculations were performed with a time step of 5 fs. The simulation time was 50 ps for the Pb24X48 systems. Since the machine time required increases drastically along with the number of electrons, the simulation of the Pb36X72 systems was carried out for 25 ps.
2019年法国巴黎索邦大学Elsa Desmaele团队000基于cp2k研究熔融碳酸盐的原子模拟：Li2CO3-Na2CO3-K2CO3系统的热力学和传输特性，论文发表在The Journal of Chemical Physics期刊上。

2020年上海交通大学的Baode Sun团队000采用vasp通过从头算分子动力学(AIMD)模拟研究了熔融NiRe和NiAlRe合金中的元素相互作用和局部结构。论文以Element interactions and local structure in molten NiRe and NiAlRe alloys: Implications for the aggregation and partition of Re为题目发表在Acta Materialia期刊上。
2020年美国宾夕法尼亚州立大学的Jianbo Ma团队000采用从头算分子动力学 (AIMD) 来研究Ba-Bi液体的特性，采用vasp进行具有强有序趋势的Ba-Bi液体中缔合物的从头算分子动力学探索。论文以An ab initio molecular dynamics exploration of associates in Ba-Bi liquid with strong ordering trends为题目发表在Acta Materialia期刊上。

2020年美国路易斯安那州立大学地质与地球物理系的Haiyang Luo团队000
，基于vasp进行硅酸盐熔体中扩散型镁同位素分馏的第一性原理计算，论文发表在Geochimica et Cosmochimica Acta期刊上。
2020年重庆大学的Chenguang Bai团队000基于CASTEP模块对CaO-SiO2-Al2O3三元渣体系的电子结构和物理性质进行第一原理研究，论文发表在Journal of Non-Crystalline Solids期刊上。论文中中采用态密度分析、电子密度差（EDD）和Mulliken布居分析。
2020年法国的François-Xavier Coudert团队001基于cp2k研究了CdSe量子点玻璃，论文发表在JACS上。
2020年加州大学戴维分校的Qing-Zhu Yin团队000基于vasp第一性原理分子动力学研究了硅酸盐熔体和铁水之间的镁分配：对地核早期热历史的影响。论文发表在Earth and Planetary Science Letters期刊上。
2020年美国的Richard C. Remsing002基于cp2k研究了液态硅中的动态共价键，论文发表在JPCB上。
2020年日本的Takahiro Ohkubo005基于FEMTECK分子动力学模拟研究了70Li2S-30P2S5玻璃中的传导机制，并与Li7P3S11晶体进行了比较，论文发表在ACS Applied Materials & Interfaces。
2020年美国的Claudio J. Margulis团队000基于cp2k的Fist模块研究了熔融的MgCl2及其与KCl的混合物中短、中阶序的温度依赖性，论文发表在JPCB上。
2020年加州大学的De-en Jiang团队000基于cp2k中的极化离子模型(PIM)来研究NaCl-UCl3熔盐体系，论文发表在Journal of Molecular Liquids期刊上。
2020年华东理工大学的Guimin Lu000基于vasp进行了熔融MgCl2局部结构和热力学性质的第一性原理分子动力学模拟研究，论文发表在Journal of Molecular Liquids。
2020年华东理工大学的Guimin Lu团队000基于vasp进行了液态MgCl2-KCl中成分依赖的微观结构演变：第一性原理分子动力学研究，论文发表在Journal of Molecular Liquids期刊上。The initial configurations were generated by packing ions randomly into given simulation cells using the Packmol code. Cells containing 140 atoms were prepared and their volumes were primarily estimated by the experimental density at a particular temperature. Each of the simulation cells was launched at 2000 K to initially equilibrate the initial configurations. It was feasible to create converged liquid structures from high-temperature calculations within timescales of up to 10 ps at 2000 K with FPMD. RDFs showed that randomly placed ions by Packmol were arranged in the form of ordered states. Then the high-temperature liquids were quenched at a rate of 180 K/ps to 1073 K.
2020年华东理工大学的Yun Ding000基于cp2k进行水-离子相互作用的第一性分子动力学研究：以稀释的 CsI 溶液为例，论文发表在Chemical Physics Letters期刊上。
2020年南京大学的Xiandong Liu团队000基于cp2k研究了Zr4+/Hf4+/Nb5+/Ta5+在硅酸盐熔体中的配位，论文发表在Chemical Geology。
2020年南京大学南京大学的刘显东团队000基于cp2k中的QS模块进行了氯化锡 (IV) 挥发过程中的锡同位素分馏：实验室实验和量子力学计算研究，论文发表在Geochimica et Cosmochimica Acta期刊上。
2020年上海应用物理研究所的Xuejiao Li团队000基于vasp使用FPMD模拟深入了解熔融MgCl2-NaCl-KCl与杂质水的动态相互作用，论文发表在Journal of Molecular Liquids期刊上。The detailed simulation process is as follows: First, 10,000 steps of simulations are carried out using the NVT ensemble with a Nosé thermostat to bring the system to a specified temperature of 773 K. The integration time step is set to 1 fs for each molten hMNK system. Then, another 5000 steps of simulations are conducted in NPT ensemble with a step time of 1 fs to optimize cell volumes using Langevin thermostat by the method of Parrinello and Rahman. Each equilibrium cell volume (V) in Table 1 is evaluated from the average value of last 3000 steps and used as an input of the following NVT simulations. Finally, the reproductive FPMD simulations within NVT ensemble are conducted for 10 ps with the same step time.
2020年美国密苏里大学Wai-Yim Ching团队000基于vasp进行了单一和离子交换碱铝硅酸盐玻璃水解效应的从头算研究，论文发表在JPCB上。
2020年越南的Ngoc Lan Mai等人000基于cp2k模拟阐明了四乙基铵在硅酸盐缩合反应中的作用，论文发表在JPCB上。
2020年爱沙尼亚的Vladislav B. Ivaništšev团队000基于cp2k模拟研究了石墨烯-离子液体界面电位降，论文发表在JPCC上。
2020年加拿大的Christopher I. Maxwell团队000基于cp2k进行熔融锂、钠和硝酸钾的可极化原子间电位的开发，论文发表在JPCB上。
2020年德国的Ashour A. Ahmed团队000基于cp2k研究了针铁矿-水界面处pH值和磷酸盐结合之间相互作用的分子水平图，论文发表在PCCP上。
2020年美国华盛顿州立大学的Maxime Pouvreau团队000基于cp2k研究了碱性溶液中Al3+二聚化的机理，论文发表在Inorganic Chemistry期刊上。
2020年澳大利亚卧龙岗大学A. Kiet Tieu团队000基于VASP研究了Fe2O3滑动界面上碱金属硼酸盐的结构响应：碱金属阳离子的影响，论文发表在Computational Materials Science期刊上。
2020年澳大利亚卧龙岗大学A. Kiet Tieu团队000基于vasp研究了由硅酸钠玻璃润滑的滑动氧化铁表面的摩擦化学，论文发表在Applied Surface Science期刊上。
2020年澳大利亚卧龙岗大学Hongtao Zhu团队000进行了硼酸盐与氧化铁界面高温物理化学相互作用的从头算研究，论文发表在Chemical Physics期刊上。
2020年中国科学院地质与地球物理研究所地球与行星物理重点实验室的Zhigang Zhang团队000基于机器学习和vasp进行地球核心条件下固态铁和液态铁之间硫的分配：来自具有机器学习势的原子模拟的约束，论文发表在Geochimica et Cosmochimica Acta期刊上。
2020年澳大利亚英联邦科学工业研究组织（CSRIO）的Yuan Mei团队000基于原位同步加速器XAS实验和cp2k的QS模块研究了硫在热液流体中钼传输中的作用，论文发表在Geochimica et Cosmochimica Acta期刊上。
2020年美国杨百翰大学的Benjamin A. Frandsen团队000基于cp2k研究了熔盐 (LiF) 0.465 (NaF) 0.115 (KF) 0.42 (FLiNaK) 的结构，论文发表在Journal of Nuclear Materials期刊上。
2020年俄罗斯叶卡捷琳堡高温电化学研究所Irina D. Zakiryanova团队000基于cp2k进行 YbCl3-KCl 和 Yb2O3-YbCl3-KCl 离子熔体的从头算分子动力学模拟和拉曼光谱研究，论文发表在Journal of Molecular Liquids期刊上。
2020年俄罗斯叶卡捷琳堡高温电化学研究所Zakiryanov, Dmitry团队000基于cp2k的QS模块进行了熔融卤氧化铅 Pb3O2X2 (X = Cl, Br, I) 的从头算分子动力学模拟，论文发表在Physics and Chemistry of Liquids期刊上。
2020年美国橡树岭国家实验室的Santanu Roy000基于cp2k的QS模块计算了熔融碱金属氯化物盐的结构和动力学，论文发表在PCCP期刊上。To provide the best possible initial configuration for these runs, we first equilibrated 149 ion pairs in the NPT ensemble at 1 bar using the PIM. We then selected an equilibrated configuration from the NPT trajectory for which the density coincided with the experimental one and used it as initial structure for subsequent AIMD.
2020年美国斯坦福大学的Thomas P. Devereaux团队000基于vasp研究SiO2锂化的从头算分子动力学研究, 论文发表在Chemical Physics Letters。
AIMD calculations were performed at high temperatures, in particular 900 K, 1050 K, 1200 K, 1350 K and 1500 K. Substantial mixing of Li with the Si and O atoms in SiO2 can be observed at these high temperatures after -6000 AIMD time steps, where each step is 1.5 fs.
2020年意大利Abdus Salam理论物理中心的Emiliano Poli团队000基于cp2k研究了铝硅酸盐和铝锗酸镁橄榄石纳米管的端接效应：密度泛函理论研究，论文发表在crystals期刊上。
2020年德国波恩大学的Barbara Kirchner团队000基于cp2k从 Ab Initio 分子动力学看成分和水含量对氯化胆碱/乙二醇深共熔溶剂的影响，论文发表在JPCB期刊上。
2020年美国圣母大学的Edward J. Maginn团队000基于cp2k和lammps研究了深共熔溶剂乙炔的液体结构和输运性质，论文发表在JPCB期刊上。
2020年英国布里斯托尔大学的David M. Sherman团队000基于cp2k的QS模块研究了 海相铁锰结壳中 Cu 的同位素不平衡：来自 Cu 同位素分馏对水钠锰矿吸附的 ab initio 预测的证据，论文发表在Earth and Planetary Science Letters期刊上。
2020年南方科技大学的Yang-Gang Wang团队000基于cp2k进行了Mn-N4/C 单原子催化剂上氧还原反应的机理洞察：溶剂环境的作用，论文发表在JPCC期刊上。
2020年西班牙巴斯克大学的Elixabete Rezabal团队000基于cp2k研究了离子液体对木质素的溶剂化：阳离子的作用，论文发表在Journal of Molecular Liquids期刊上。
2020年西北农林大学的Hanzhong Jia团队000基于cp2k对锑 (V) 溶液化学的原子级理解：第一性原理分子动力学模拟的见解，论文发表在Inorganic Chemistry期刊上。
2020年俄罗斯莫斯科大学的Olga A. Syzgantseva团队000基于cp2k从第一性原理对聚合物金属交联的结构洞察：钙-聚甲基丙烯酸案例研究，论文发表在Polymer期刊上。
2020年美国加州大学Riccardo Dettori团队000基于cp2k研究深地球条件下水溶液中的二氧化碳、碳酸氢根和碳酸根离子，论文发表在PCCP期刊上。
2020年瑞士苏黎世大学化学系的Vladimir V. Rybkin团队000基于cp2k研究了溶剂化电子还原水性二氧化碳的机理，论文发表在JPCB期刊上。
2020年印度理工学院的Amalendu Chandra团队000基于cp2k进行了碘酸盐离子溶剂化壳中水的动力学：Born-Oppenheimer 分子动力学研究，论文发表在JPCB期刊上。
2020年法国雷恩大学的Siham Kamali-Bernard团队000基于cp2k进行了水-硅酸三钙界面质子转移的从头算分子动力学描述，论文发表在
Cement and Concrete Research期刊上。

2021年日本名古屋工业大学的Tomoyuki Tamura团队021基于DL_POLY和vasp研究了硅磷酸盐玻璃中质子和钠离子的扩散：基于第一性原理分子动力学模拟的见解，论文发表在PCCP上。
2021年北京科技大学钢铁共性技术协同创新中心的Lijun Wang团队000基于vasp进行溶解 S 的液态 FeO-SiO2 硅酸盐体系的Ab Initio分子动力学模拟，论文发表在MMTB期刊上。
2021年中南大学的Hesong Li006基于vasp5.2.11模拟了高硅含量Na3AlF6–Al2O3–SiO2熔盐体系，论文发表在ACS Omega上。
2021年麻省理工学院的Boris Khaykovich团队000基于vasp研究熔融 NaCl-CrCl3 盐的复杂结构：Cr-Cl八面体网络和中序，论文发表在ACS Applied Energy Materials期刊上。
2021年美国佐治亚理工学院的Jacob Startt团队000基于vasp进行了吸附盐对镍 (100) 表面 Cr 偏析影响的从头算研究，论文发表在
Applied Surface Science期刊上。
2021年日本的Takahiro Ohkubo等人003基于FEMTECK研究了硼硅酸锂玻璃的结构和动力学，论文发表在JPCC上。
2021年法国巴黎萨克雷大学的Hicham Jabraoui等人004基于CPMD和vasp进行了“对控制硼硅酸盐玻璃-水界面事件的原子洞察”研究，论文发表在JPCC上。
2021年Luigi Giacomazzi000在JPCC上发文“碱磷酸盐玻璃中的顺磁本征点缺陷：解开P3中心原点和局部环境影响”，对作为碱性磷酸盐玻璃代表的P2O5和Na2O-P2O5 玻璃中的固有顺磁点缺陷进行了第一性原理研究。
2021年浙江大学的Haiming Gong023基于vasp从头算分子动力学模拟研究了SiO2-B2O3-Al2O3-Na2O硼铝硅酸盐玻璃体系的结构性质和电子性质，论文发表在JACE上。
2021年华中科技大学的Xiaowei Liu团队000基于vasp进行了银改性硅酸钒吸附元素汞的机理研究，论文发表在Journal of Hazardous Materials。
2021年美国的Katie A. Maerzke团队000进行了CuCl在高温水蒸气中的第一性原理模拟，论文发表在JPCB期刊上。
2021年加州大学的Mark Asta团队017基于lammps和vasp进行了熔融氟化盐中铬溶剂化的从头算模拟研究，论文发表在Journal of Molecular Liquids期刊上。
2021年英国剑桥大学的Thomas D. Bennett团队000基于cp2k中的QS模块研究了混合有机-无机钙钛矿的熔化，论文发表在Nature Chemistry期刊上。
2021年南京大学的刘显东团队000基于cp2k中的QS模块进行了水热条件下氯化锡 (II) 形态和平衡锡同位素分馏的第一性原理研究，论文发表在Geochimica et Cosmochimica Acta。
2021年南京大学的Hai-Zhen Wei团队000研究了硼配位和 B/Si 排序控制矿物、熔体和流体之间的平衡硼同位素分馏，论文发表在Chemical Geology，这篇论文中对同位素的平衡分馏以及从头算模拟进行了大量的综述，注意检索。
2021年澳大利亚卧龙岗大学的A. Kiet Tieu团队000基于vasp进行了氧化铁表面杂质增强粘附和石墨烯润滑性的第一性原理研究，论文发表在JPCC期刊上。
2021年中国科学院大学计算地球动力学重点实验室Yongbing Li团队000基于Quantum Espresso进行铜矿物中铜同位素的平衡分馏：第一性原理研究，论文发表在Chemical Geology期刊上。
2021年中国科学院地球化学研究所矿床地球化学国家重点实验室Yun Liu团队000基于vasp进行了平衡溴同位素分馏的第一性原理计算，论文发表在Geochimica et Cosmochimica Acta期刊上。
2021年南京大学地球科学与工程系Hai-Zhen Wei团队000基于CASTEP模块进行氯同位素地幔异质性：理论第一性原理计算的限制研究，论文发表在Chemical Geology期刊上。
2021年法国的S. Rabin团队000基于Quantum Espresso进行了岩浆温度下含铁矿物之间铁和硅同位素分馏的第一性原理计算：第二原子邻居的重要性，论文发表在Geochimica et Cosmochimica Acta期刊上。
2021年美国西北太平洋国家实验室的Manh-Thuong Nguyen团队000基于cp2k中的QS模块进行了(K,Li)Cl 和 (K, Na)Cl 熔盐混合物中热力学和传输特性的从头算分子动力学评估，论文发表在Journal of Molecular Liquids期刊上。该论文中基于平均力势来评价PMF平均力势来评价阴阳离子的解离能。
2021年美国普林斯顿大学的Anirban Mondal000基于cp2k研究了碳酸盐-氢氧化物电解质中传输机制的第一性原理建模，论文发表在JPCC期刊上。为了在 923.15 K 下获得预先平衡的配置，我们首先使用 GROMACS 模拟程序在P = 1 bar的NPT集合中执行经典 MD 模拟。运动方程以 0.5 fs 的时间步长进行积分。 根据 Martyna 等人的方案，系统在 1 bar 的等温-等压 (NPT) 集合中平衡 8 ps，时间常数为 250 fs。 平衡之后是在规范 NVT 集合中进行 65 ps 的生产模拟。 我们将温度设置为 923.15 K，由一组六个 Nosé–Hoover 恒温器 (30) 控制，时间常数为 100 fs。
2021年波兰克拉科夫AGH科技大学的Paweł Stoch团队000基于CPMD研究了铝-磷酸盐（Al2O3–P2O5）玻璃网络，论文中进行了Mayer的键序分析、ELF分析，论文发表在Ceramics——International期刊上。这篇论文比较重要，首先是采用了与cp2k接近的CPMD第一性原理计算方法，其次模拟体系是Al2O3–P2O5玻璃网络，最后首次在玻璃体系中进行电子结构分析，对于未来的工作有较大的借鉴意义。
2021年美国橡树岭国家实验室的Santanu Roy等人000基于cp2k的QS模块进行了MgCl2和ZnCl2熔盐的局域结构，论文发表在JPCB上。 To generate the initial structures for AIMD simulations, we performed PIM-based(35,59) 1 ns equilibrium molecular dynamics (MD) simulations for pure MgCl2 (comprising 149 Mg2+ and 298 Cl– ions) and the 50%/50% mixture of MgCl2 and KCl (comprising 100 Mg2+, 100 K+, and 300 Cl– ions) at 1073 K and 1 bar in the isothermal–isobaric ensembles (NPT). Structures with the average equilibrated densities (1.612 g cm–3 for pure MgCl2 and 1.573 g cm–3 for the mixture(35)) were extracted as initial configurations for AIMD simulations. All AIMD simulations were performed in the NVT ensemble (constant temperature and volume) using different aforementioned DFT functionals for at least 80 ps with a time step of 1.0 fs. A Nosé–Hoover chain thermostat(63,64) was set to a chain length of 3 with a velocity rescaling time constant of 1.0 ps. The first 20 ps were discarded and the remaining part of the trajectory was used for analysis.
2021年德国多特蒙德工业大学的Mirko Elbers团队000基于cp2k的QS模块进行了热液 NaCl水溶液中的离子缔合：对超临界水微观结构的影响，论文发表在PCCP期刊上。
2021年法国PSL University的Rodolphe Vuilleumier团队000基于cp2k的QS模块研究熔融碳酸盐电解槽环境中至关重要的分子和离子的溶剂化，论文发表在International Journal of Hydrogen Energy期刊上。
2021年中科大李震宇团队000基于cp2k进行水纳米液滴中羟基自由基的迁移率和溶剂化结构：Born-Oppenheimer 分子动力学研究，论文发表在PCCP上。
2021年福州大学的Lilong Jiang团队000基于cp2k和Gaussian研究深共熔溶剂通过酸碱协同作用和强氢键相互作用高效选择性分离氨，论文发表在Journal of Molecular Liquids期刊上。
2021年法国的Sylvia M. Mutisya团队000基于cp2k的QS模块进行从增强的 Ab Initio 分子动力学模拟预测硅酸盐的碳化反应机理，论文发表在MDPI旗下的Minerals期刊上。
2021年印度尼西亚卡渣玛达大学的Fajar I. Pambudi团队000基于cp2k的QS模块研究了含芳香族化合物的硫黄铁矿型沸石的结构，论文发表在Materials Today Communications期刊上。
2021年印度理工学院的Hemant K. Kashyap团队000基于cp2k的QS模块进行AIMD模拟揭示了 Reline 和 Ethaline 深共晶溶剂中CO2和SO2的不同溶剂化结构，论文发表在JPCB期刊上。
2021年美国明尼苏达大学的J. Ilja Siepmann团队000基于cp2k从第一原理模拟 PH3、AsH3和 SbH3 的汽液平衡，论文发表在JPCC期刊上。
2021年巴西利亚大学的B.J.C. Cabral团队000基于cp2k研究了碱金属原子与碳量子点相互作用的第一原理方法，论文发表在Computational Materials Science期刊上。
2021年瑞士苏黎世大学化学系Jinggang Lan团队000基于cp2k进行了溶剂化电子的量子动力学研究，论文发表在Nature Communications期刊上。
2021年中南大学的Hongliang Zhang团队000基于cp2k进行CaF2-2.2NaF-AlF3体系微观结构和扩散性能的从头分子动力学研究，论文发表在Chemical Physics期刊上。
2021年法国巴黎文理研究大学的François-Xavier Coudert团队000基于DL_POLY和CP2K研究了玻璃成分对 CdSe 量子点掺杂玻璃发光机制的影响，论文发表在JPCC期刊上。

cp2k态密度(DOS)计算

2012年美国加州大学的Yuan Ping等人000基于cp2k进行了用于水氧化的WO3包合物的·第一性原理研究（Tungsten Oxide Clathrates for Water Oxidation: A First Principles Study），论文发表在Chemistry of Materials。该论文被选为cp2k官网上用于投影态密度计算教学的exercises。
2015年瑞士乌普萨拉大学Rebecka Lindblad000基于CPMD和cp2k研究了CH3NH3PbX3钙钛矿的电子结构：对卤化物部分的依赖性，论文已题目Electronic Structure of CH3NH3PbX3 Perovskites: Dependence on the Halide Moiety发表在了JPCC期刊上。
2016年荷兰的拉德堡德大学000基于cp2k研究了部分和完全氢化六方氮化硼的能量、势垒和振动光谱，论文发表在PCCP上。
2022年中国工程物理研究院核物理与化学研究所的Lei Li等人000基于lammps和cp2k进行无定形二氧化硅中硅空位相关缺陷性质的第一性原理研究，论文发表在Journal of Non-Crystalline Solids期刊上。
2022年上海科技大学的Xujie Zhu000基于cp2k结合实验进行了具有锯齿形边缘的C144六角冠冕(Hexagonal Coronoid)的表面合成，论文发表在ACS Nano上。

合金熔体从头算分子模拟

2015年上海交通大学的Jiao Zhang团队000基于vasp进行从头算模拟：局部熔体结构与液态铝中 Fe、V、Ti 和 Si 的偏析行为之间的相关性，论文发表在Computational Materials Science期刊上。

2017年上海交通大学的Jiao Zhang团队000基于vasp进行第四周期过渡金属在液态铝中的迁移行为：从头算分子动力学研究，论文发表在Computational Materials Science期刊上。

2018年上海交通大学Yongbing Dai团队000基于vasp进行熔融 Ni1−xMx合金中的化学效应研究，论文发表在Computational Materials Science期刊上。
2018年加州大学Robert O. Ritchie团队000进行了CrCoNi 基高熵合金的熔体：来自ab initio模拟的 原子扩散和电子/原子结构研究，论文发表在AIP Applied Physics Letters期刊上。

2019年山东大学的Jingyu Qin团队000基于vasp进行液态二元 M-Si（M=Al、Fe、Mg 和 Au）合金体系的自扩散系数第一性原理分子动力学模拟，论文发表在AIP Advances期刊上。

2020年美国宾夕法尼亚大学的Jianbo Ma团队000基于vasp进行强有序Ba-Bi液体中缔合物的从头算分子动力学研究，论文发表在Acta Materialia期刊上。

2020年上海交通大学的Jiao Zhang团队000基于vasp进行金属铝液中化学效应与偏析行为的相关性研究，论文发表在Computational Materials Science期刊上。

2021年中科院金属所Xing-Qiu Chen团队000基于vasp进行 U-Nb 液态合金中的局部 Nb 簇：从头算分子动力学研究，论文发表在Nuclear Materials and Energy期刊上。

金属-有机框架玻璃结构

2020年法国PSL大学的François-Xavier Coudert团队000基于cp2k的QS模块研究金属-有机框架玻璃结构，论文发表在Chemistry of Materials期刊上。

相关文章

2011年北爱尔兰的Antonius P. J. Jansen团队000采取连续kMC的非晶格动力学蒙特卡罗(kMC)方法来研究硅酸盐低聚初始阶段的机理，论文发表在JACS上。

从头算两相分配

2009年中国科学院地质与地球物理研究所岩石圈演化国家重点实验室的Yigang Zhang团队000基于vasp进行了两相从头算分子动力学模拟，研究了Si和O在液态铁和硅酸盐熔体之间的分配，论文发表在Geophysical Research Letters.
MD simulations are performed in the NVT ensemble with a time step of 1 fs. Internal energy drift slope is 0.7 K/ps in the largest case, which is much smaller than fluctuations of the internal energy. Systems are generally composed of 256 atoms, with one run comprising 400 atoms to study the effect of system size (i.e., number of atoms). 1000 steps are used for scaling, ∼20000 steps for the two phases (liquid iron and silicate melt) to segregate, and another ∼35000 steps for accumulation of atom positions for composition calculations. With our current computational facilities, each run takes about 3 months to finish.

2012年中国科学院地质与地球物理研究所地球深部重点实验室的Yigang Zhang团队000基于vasp进行了两相共存第一性原理分子动力学 (MD)模拟，以研究碳和其他轻元素在金属和硅酸盐熔体之间的分配。论文发表在PNAS期刊上。
分子动力学模拟在 NVT 系综中执行，时间步长为1fs。我们的“实验电荷”由260个原子组成。起始配置是模拟单元中原子的随机分布；20,000到30,000步用于使两相（液态铁和硅酸盐熔体）分离并使系统达到平衡。在此期间，系统的总能量先下降，然后在一个平台值附近波动。然后对系统进行另外 30,000-50,000 步的模拟，以累积原子位置以进行成分计算。

2013年德国地球科学研究中心Volker Haigis团队000基于cp2k的QS模块进行了硅酸盐熔体中Y的分子动力学模拟及其对微量元素分配影响的研究，论文使用基于经典MD的方法直接计算CaO-Al2O3-SiO2系统中熔体对之间的微量元素分配，论文发表在Chemical Geology期刊上。

2017年德国地球科学研究中心的Sandro Jahn团队000基于CP2K的QS模块和FIST研究了硅酸盐熔体之间的微量元素分配，论文发表在Geochimica et Cosmochimica Acta期刊上。使用热力学积分方案来研究各种微量元素（Y、La、As）在花岗岩和辉长岩以及两种含钛熔体中的分配行为，并与实验结果进行比较。
（CP2K, PBE, GTH, DZVP, 160Ry, 100-107原子，2500K，注意其所采用的热浴方法为CSVR 热浴）

2017年德国地球科学研究中心的Sandro Jahn团队000基于cp2k的QS模块金属和硅酸盐熔体之间的镍分配，论文发表在Chemical Geology期刊上。经典分子动力学模拟获得硅酸盐熔体初始构型，模拟温度大于2500K（低于2500K不利于Si-O键断裂和形成，不利于达到平衡）。步长0.95fs，7fs for production runs ，最后5 ps用于能量和结构分析
For the silicate melts, the simulation cells contained 112 atoms of M2SiO4 with different amounts of M = Mg, Fe and Ni (see Table 1).The simulation boxes of the metal melts contained 64 atoms with varying amount of Fe and Ni (see Table 1). The temperature in the simulation (2500 K) was chosen to be relatively close to the experimental one from Thibault and Walter (1995) (2123 K). At temperatures lower than 2500 K, Si–O bonds are not sufficiently often broken or formed to reach equilibrium on the MD time scale (Spiekermann et al., 2016). A temperature of 2500 K is maintained using a Nosé-Hoover (Nosé, 1984a, Nosé, 1984b) thermostat. A time step of 0.95 fs was used for the numerical integration of the equations of motion. Initial configurations for the AIMD simulations of the silicate melts were obtained from classical molecular dynamics simulations of Mg2SiO4 melts using an advanced polarizable ion potential (Jahn and Madden, 2007). Subsequently, the metal melt was equilibrated using AIMD for 7 ps. Trajectories of production runs were collected for approximately 7 ps, of which the last 5 ps were used for structural and energetic analysis.
（GPW方法, PBE泛函, DZVP, GTH, 400Ry，112 atoms）

2020年加州大学戴维分校的Qing-Zhu Yin团队000基于vasp第一性原理分子动力学研究了硅酸盐熔体和铁水之间的镁分配：对地核早期热历史的影响。论文发表在Earth and Planetary Science Letters期刊上。
（256个原子，随机分布的原子被放置在分子动力学模拟箱中。在模拟的第一阶段，模拟箱中的原子合并成两相，一个是铁水，另一个是硅酸盐熔体。这个阶段需要大约 20,000 到 30,000 个分子动力学步骤。模拟继续进行另外 30,000 到 50,000 步以累积原子位置。金属相的组成是通过使用计算几何的方法从累积的原子位置计算出来的，然后可以推导出硅酸盐相的组成，因为整体组成是已知的。）

2021年南京大学的Hai-Zhen Wei团队000研究了硼配位和 B/Si 排序控制矿物、熔体和流体之间的平衡硼同位素分馏，论文发表在Chemical Geology，这篇论文中对同位素的平衡分馏以及从头算模拟进行了大量的综述，注意检索。

山东大学

专业名词

interatomic potential molecular dynamics (IPMD)

cp2k相关高被引

vasp相关高被引

常看的重要论文

2006年英国伦敦大学学院的Antonio Tilocca团队000基于DL_POLY和CPMD进行45S5 生物活性硅酸盐玻璃的 Ab Initio 分子动力学研究，利用 CPMD 模拟的准确性来深入了解短程结构并分析该生物材料的振动和电子特性。该作者研究方向为生物活性玻璃从头算研究，注意关注该作者文章。

2014年日本同步辐射研究所的Shinji Kohara团队000基于高能 X 射线衍射测量和基于cp2k的DF-MD 模拟研究了极易碎液体的原子和电子结构，论文发表在nature communication上。（ZrO2体系，GTH赝势，BASIS_MOLOPT基组，PBE泛函，400Ry截断）； 初始原子配置是通过反向蒙特卡罗 (RMC) 模拟创建的，该模拟具有 18.98 Å 立方体中 501 个原子的高能 X 射线衍射数据（实验密度，每 Å3 为 0.0733 个原子）。使用了 RMC++ 程序代码。 Born-Oppenheimer MD 模拟是使用 Nóse-Hoover 恒温器进行的，时间步长为 2 fs（初始化）和 1 fs（数据收集）。 该系统在 3,100 K（—2,800 °C）下模拟，总共 30 ps，其中最后 10 ps 用于数据收集。相应的原子均方位移清楚地显示出液体行为（扩散）。

2014年伦敦大学的Jamieson K.Christie团队000基于cp2k的QS模块进行与在生物活性磷酸盐玻璃中掺入氟相关的结构变化的从头算分子动力学模拟，论文发表在Biomaterials期刊上。初始结构随机均匀分布，2500K，NVT系综条件下模拟20ps,通过计算均方位移确定是否达到平衡，冷却通过梯度降温平衡模拟至目标温度。（P2O5-Na2O-CaO-CaF2体系，363/197原子数，GGA，PBE，DZVP基组，700Ry，时间步长1fs） For each composition, an MD run was started from the initial quasi-random configuration in the NVT ensemble at 2500 K until the model was well equilibrated, which was confirmed by computing the actual and mean-square atomic displacements. This typically took 20 ps of MD simulation time. Then, each model was run for 10 ps in NVT ensembles at each of the following temperatures: 2200 K, 1900 K, 1600 K, 1300 K, 1000 K, 750 K and 500 K, before being run for 20–25 ps in the NVT ensemble at 300 K. The production run, overwhich all data given in this paper are averaged, constitutes the last two-thirds of this room-temperature run.

2016年英国伦敦大学的Konstantinos Konstantinou团队000基于cp2k使用ab initio分子动力学模拟模拟(SiO2)57.5–(B2O3)10–(Na2O)15–(CaO)15–(MoO3)2.5和(SiO2) 57.3–(B2O3)20–(Na2O)6.8–(Li2O)13.4–(MoO3)2.5体系核废料玻璃中钼的局部原子结构，论文发表在PCCP期刊上。（PBE，DZVP，700Ry，Nosé–Hoover thermostat）对于每种组合物，初始配置在2300K下加热，AIMD运行时间为25ps，以确保系统在此温度下熔化并达到良好平衡。尽管总能量有小幅漂移，但记录的能量波动低于0.001%。随后使用逐步工艺冷却熔融结构，包括一系列九个NVT AIMD各运行 10 ps，目标温度设置为 2000 K、1800 K、1600 K、1400 K、1200 K、1000 K、800 K、600 K 和 300 K。在 300 K 时，结构进一步平衡 10 ps ，然后是 10 ps 的最终 AIMD 生产运行，以收集结构数据。该计算方案对应于 135 ps的总模拟时间和大约 20 K ps-1的标称冷却速率。

2017年英国伦敦大学的J. K. Christie团队000基于GULP软件拟合Buckingham势，DLPOLY代码进行分子模拟，CP2K进行第一性原理计算研究锶夹杂物对磷酸盐基玻璃生物活性的影响。论文并未说明所采用的具体基组和泛函。 The models were then run in an NVT ensemble at 2500 K for 20 ps, before being cooled in a series of runs in NVT ensembles of length 10 ps, decreasing the temperature by about 300 K in each run, corresponding to a cooling rate of ∼30 K/ps. The models were then run in an NVT ensemble at 300 K for ∼40–50 ps, the final two-thirds of which formed the production run.

2018年华盛顿州立大学的S. Banerjee团队000基于cp2k的QS模块进行了Na2S+SiS2玻璃电解质中结构和Na+离子传输的分子动力学建模，论文发表在JPCB上。熔体淬火技术包括将系统初始加热到3000K的高温，并将熔体在此温度下保持100fs以随机化熔体。然后以10^14K/s的非常高的淬火速率将熔体淬火至室温。淬火玻璃在室温下在NVT（恒定原子数、体积和温度）集合下平衡10ps，然后生产运行30ps。对于生产运行，平衡期间的时间步长分别为0.1和1fs。
（Na2S+SiS2/ 144 ion/ 1250 Ry 截断）
no ion type basis set pseudopotential
1 Na DZVP-SR-MOLOPT-GTH GTH-PBE-q9
2 Si TZVP-MOLOPT-GTH GTH-PBE-q4
3 S TZV2P-MOLOPT-GTH GTH-PBE-q6

2019年美国华盛顿州立大学的S. Banerjee团队000基于cp2k的QS模块通过从头分子动力学研究 Na2S + P2S5玻璃电解质的结构及其对 Na+ 离子电导率的影响。论文发表在Solid State Ionics期刊上。熔体淬火技术包括将系统初始加热到 1500K 形成熔体，并将熔体在此温度下保持 10 ps以随机化熔体。 然后，我们以 10^14K/s 的非常高的淬火速率将熔体淬火至室温。 我们最初在 NPT（恒定原子数、压力和温度）集合下在室温 (300K) 下平衡淬火玻璃 100ps，以达到平衡密度。 然后该系统在 NVT（恒定原子数、体积和温度）集合下在 300K 下进一步平衡 100ps，以去除淬火过程中的任何残余应力。 最后，我们通过分析系统在 NVT 集合下 40ps 的生产运行轨迹来分析所有结构和离子传输特性。
（Na2S + P2S5/ 144 ions/ ）
no Ion type Basis set Pseudopotential
1 Na DZVP-SR-MOLOPT-GTH GTH-PBE-q9
2 P TZVP-MOLOPT-GTH GTH-PBE-q4
3 S TZVP-MOLOPT-GTH GTH-PBE-q6

2019年美国加州大学的Nazanin Rahimi团队Properties of Negatively Charged Ruthenium Clusters in Molten Sodium Chloride基于vasp采用密度泛函理论和分子动力学研究了分散在熔融 NaCl 中的带负电的钌簇，论文发表在JPCC上。

2020年美国杨百翰大学的Benjamin A. Frandsen团队000基于cp2k研究了熔盐 (LiF) 0.465 (NaF) 0.115 (KF) 0.42 (FLiNaK) 的结构，论文发表在Journal of Nuclear Materials期刊上。论文中省略了部分模拟细节。 Simulations were performed in the NPT ensemble, with pressure held at atmospheric pressure through the method of Ref. [23] and temperature held constant through the Nose-Hoover thermostat. All simulations had a time step of 0.5 fs. The simulation cell was considered equilibrated if the average density over a period of 5 ps (10,000 steps) was within 2% of the average density of the next 5 ps. The NPT ensemble is sensitive to relatively small errors in calculated energy, and so high energy cutoffs were required for predicted densities to converge: 2000 Ry for the plane-wave energy cutoff, and 120 Ry for the relative cutoff.

2020年澳大利亚英联邦科学工业研究组织（CSRIO）的Yuan Mei团队000基于原位同步加速器XAS实验和cp2k的QS模块研究了硫在热液流体中钼传输中的作用，论文发表在Geochimica et Cosmochimica Acta期刊上。 Periodic boundary conditions were applied in the ab initio MD simulations. We performed simulations at 77°C, 200°C and 300°C at 800bar to compare with the XAS experiments, and at near-saturation pressure at 200°C (20bar) and 300°C (100bar). The simulations were run in the NVT ensemble with the temperature controlled by the Nosé thermostat for both the ions and electrons. For these constant volumes calculations, the densities were taken from the equations of state of a 2 molal NaCl solution. Ab initio molecular dynamics simulations were performed using the CP2K/QUICKSTEP package. The BLYP functional generally provides a good description of the structural properties of water including O-O interactions, H-bond statistics, angular distributions, coordination numbers, and radial distribution functions when compared with experiments. Therefore, here we performed simulations at 77°C (350K) to reproduce the solution at room temperature. A simulation timestep of 0.5 femtosecond (fs, 10−15 second) was chosen for all simulations. To achieve good sampling of the structural properties over simulation time , all the simulations were calculated for 9–15ps including a 2ps initial equilibration. As discussed previously , ab initio MD simulations are computationally intensive. Ab initio MD studies published by researchers from various groups indicate that it is viable to represent the geometrical and thermodynamic properties of solute and solvent molecules within manageable simulation times of tens of picoseconds.
（Mo, Na, S, O and H元素，280Ry, GTH赝势，DZVP基组，）

2020年南京大学的Xiandong Liu团队000基于cp2k研究了Zr4+/Hf4+/Nb5+/Ta5+在硅酸盐熔体中的配位，论文发表在Chemical Geology。The model silicate melt contains 16 NaAlSi3O8 (albite) units. Each MD simulation was performed with a time step of 0.5 fs. The simulations of anhydrous systems in Table 1 were carried out in canonical (NVT) ensemble. We first obtained the albite melt by performing a simulation at 5000 K for 10.0 ps. Then the melt was quenched to 3000 K. The simulation at 3000 K lasted over 30.0 ps. For the other systems, in order to achieve reasonable equilibration, we performed restrained simulations to prevent the ligands (i.e., O and F) from running away from Si/Al/HFSE in the equilibration step. The Si/Al—F or HFSE—O/F bonds were restrained by using harmonic potentials. All of the restrained simulations were firstly performed at 5000 K for 10.0 ps. Then these systems were quenched to 3000 K. The simulations with the same restraints were performed for 10.0 ps. After that, the production runs without the restraints were performed at 3000 K for over 30.0 ps.
(GTH, DZVP, 600Ry, )

2020年俄罗斯叶卡捷琳堡高温电化学研究所Irina D. Zakiryanova团队000基于cp2k进行 YbCl3-KCl 和 Yb2O3-YbCl3-KCl 离子熔体的从头算分子动力学模拟和拉曼光谱研究，论文发表在Journal of Molecular Liquids期刊上。论文模拟细节未交代各原子数，初始构型是什么样的，如何获得的，有没有高温弛豫消除初始构型记忆。Simulations were performed under periodic boundary conditions in the cell of a fixed volume. The temperature was 973 K for systems without oxygen and 1073 K for systems with oxygen.The simulation time was 50, 25, 20 and 15 ps for Yb13, Yb13O, Yb50 and Yb50O correspondingly. In our simulations, the total number of electrons vary from 910 for Yb13 to 1103 for Yb50O. This forces us to limit the simulation runs to the lengths of thousands of time steps. Therefore, we will not discuss properties requiring long runs, such as self-diffusion coefficients and bond lifetimes. The MD time step was chosen to be 5 femtoseconds for Yb13O and Yb50O melts and 10 femtoseconds for Yb13 and Yb50 melts, ensuring smooth movement of all kinds of atoms.

2020年美国橡树岭国家实验室的Santanu Roy000基于cp2k的QS模块计算了熔融碱金属氯化物盐的结构和动力学，论文发表在PCCP期刊上。We first equilibrated 149 ion pairs in the NPT ensemble at 1 bar using the PIM classical foecefield to provide the best possible initial configuration for these runs. We then selected an equilibrated configuration from the NPT trajectory for which the density coincided with the experimental one and used it as initial structure for subsequent AIMD.Trajectories were run in the NVT ensemble using the Nosé–Hoover thermostat with a 1 ps time constant, a time step of 0.5 fs was used to generate a 120 ps trajectory and the last 100 ps of that trajectory were used for computing S(q) and D(r).

2020年俄罗斯叶卡捷琳堡高温电化学研究所Zakiryanov, Dmitry团队000基于cp2k的QS模块进行了熔融卤氧化铅 Pb3O2X2 (X = Cl, Br, I) 的从头算分子动力学模拟，论文发表在Physics and Chemistry of Liquids期刊上。 A two-stage AIMD simulation having periodic boundary conditions was carried out to obtain information on the structure of molten lead oxyhalides Pb3O2X2 (X = Cl, Br, I). The distribution of ions in the cell at the end of the preliminary calculation was then used as a starting point in the subsequent calculation having a fixed volume. The first stage was to determine the density of the melts at a constant pressure. The second main stage was to obtain data on the local structure and other characteristics. For the density of melts, AIMD was performed as follows: pressure – 1 bar; temperature – 973 K (NPT ensemble); time step – 5 fs; simulation time – 50 ps. The Nose thermostat was applied, having a time constant of 1500 fs. At the second stage, the calculation was carried out at a constant predetermined equilibrium cell volume and a temperature of 973 K (NVT ensemble). The simulation time was 65 ps, while the timestep was 5 fs. The thermostat time constant was 100 fs. The data from this run were used to determine properties of the melts.We previously showed that for PbX2 melts, an increase in the size of the simulated system from 72 to 108 particles does not significantly affect the results of the calculation of the local structure; thus, 72 was adopted as a sufficient number of ions for the purposes of the experiment.

2020年法国的François-Xavier Coudert团队001基于cp2k研究了CdSe量子点玻璃，论文发表在JACS上。（39Na2O–78SiO2–33CdSe，共417个原子体系，GGA，PBE，600Ry； 对于Na、Cd 和Se，DZVP-MOLOPT-SR-GTH基组；对于O和Si，DZVP- MOLOPT-GTH基组）。 After an initial geometry optimization with DFT, the resulting relaxed structure (Figure 1c) was used as the initial structure for the AIMD simulations. The structure was quenched from 500 to 300 K in steps of 50 K, with a total 10 ps AIMD run at a time step of 2 fs. The production run was conducted in the NVT ensemble at 300 K for 10 ps.

2021年美国普林斯顿大学的Anirban Mondal000基于cp2k研究了碳酸盐-氢氧化物电解质中传输机制的第一性原理建模，论文发表在JPCC期刊上。A system originally composed of 120 Li+, 80 K+, 80 CO32–, 40 OH– ions, and 10 CO2 molecules was studied a to investigate the chemical routes for the formation/dissociation of different chemical species and their role in the transport mechanism. The temperature was set to 923.15 K. The GROMACS simulation program was wsed to performed classical MD simulations in the NPT ensemble at P = 1 bar to obtain a pre-equilibrated configuration at 923.15 K. The equilibrated supercell from the classical MD trajectory was geometry optimized within the density functional theory (DFT) framework at the same level of theory as described below. Equations of motion were integrated with a time step of 0.5 fs. The system was equilibrated for 8 ps in the isothermal–isobaric (NPT) ensemble at 1 bar by using an isotropic unit cell according to the scheme of Martyna et al., with a time constant of 250 fs. Equilibration was followed by a production simulation in the canonical NVT ensemble for 65 ps. We set the temperature at 923.15 K, controlled by one chain of six Nosé–Hoover thermostats(30) with a time constant of 100 fs. Three-dimensional periodic boundary conditions were employed in all simulations.
(120 Li+, 80 K+, 80 CO32–, 40 OH–离子, 和10 CO2分子，共630个原子体系，PBE泛函， GTH赝势，DZVP基组，400Ry)

2021年美国西北太平洋国家实验室的Manh-Thuong Nguyen团队000基于cp2k中的QS模块进行了(K,Li)Cl 和 (K, Na)Cl 熔盐混合物中热力学和传输特性的从头算分子动力学评估，论文发表在Journal of Molecular Liquids期刊上。 AIMD simulations were carried out within the NVT ensemble . The temperature was maintained by the canonical sampling through stochastic velocity rescaling thermostat . A time step of 1.5 fs was used. Each initial structure was generated at random, then equilibrated by a 20-ps simulation. Results reported in this work were obtained from 45 ps production AIMD runs. As mentioned above, we investigated two mixtures, (K,Li)Cl and (K,Na)Cl, in which the molar fraction of KCl, xKCl, is 0, 0.25, 0.50, 0.75, and 1. Systems of unit cells of 512 atoms were adopted. At each simulation temperature, we used atomic densities determined with experiments reported by Van Artsdelen and Yaffe. Different simulation temperatures were investigated, 1173, 873, 673 K (for the (K,Li)Cl mixture) and 1173, 1073, and 973 K ((K,Na)Cl).

2021年美国橡树岭国家实验室的Santanu Roy等人000基于cp2k的QS模块进行了MgCl2和ZnCl2熔盐的局域结构，论文发表在JPCB上。 To generate the initial structures for AIMD simulations, we performed PIM-based 1 ns equilibrium molecular dynamics (MD) simulations for pure MgCl2 (comprising 149 Mg2+ and 298 Cl– ions) and the 50%/50% mixture of MgCl2 and KCl (comprising 100 Mg2+, 100 K+, and 300 Cl– ions) at 1073 K and 1 bar in the isothermal–isobaric ensembles (NPT). Structures with the average equilibrated densities (1.612 g cm–3 for pure MgCl2 and 1.573 g cm–3 for the mixture(35)) were extracted as initial configurations for AIMD simulations. All AIMD simulations were performed in the NVT ensemble (constant temperature and volume) using different aforementioned DFT functionals for at least 80 ps with a time step of 1.0 fs. A Nosé–Hoover chain thermostat(63,64) was set to a chain length of 3 with a velocity rescaling time constant of 1.0 ps. The first 20 ps were discarded and the remaining part of the trajectory was used for analysis.
（(K, Cl, Li, Na原子体系, 298个原子体系)，GTH赝势，PBE-D3校正，DZVP基组，440Ry）

2021年波兰克拉科夫AGH科技大学的Paweł Stoch团队000基于CPMD研究了铝-磷酸盐（Al2O3–P2O5）玻璃网络，论文中进行了Mayer的键序分析、ELF分析，论文发表在Ceramics International期刊上。这篇论文比较重要，首先是采用了与cp2k接近的CPMD第一性原理计算方法，其次模拟体系是Al2O3–P2O5玻璃网络，最后首次在玻璃体系中进行电子结构分析，对于未来的工作有较大的借鉴意义。模拟过程中采用随机均匀分布模型，然后按梯度降低温度，在不同温度下进行平衡模拟，至目标温度。
For each composition, an MD run was started from the initial quasi-random configuration in the NVT ensemble at 2500 K until the model was well equilibrated, which was confirmed by computing the actual and mean-square atomic displacements. This typically took 20 ps of MD simulation time. Then, each model was run for 10 ps in NVT ensembles at each of the following temperatures: 2200 K, 1900 K, 1600 K, 1300 K, 1000 K, 750 K and 500 K, before being run for 20–25 ps in the NVT ensemble at 300 K. The production run, overwhich all data given in this paper are averaged, constitutes the last two-thirds of this room-temperature run.

2021年浙江大学的Haiming Gong000基于vasp从头算分子动力学模拟研究了SiO2-B2O3-Al2O3-Na2O硼铝硅酸盐玻璃体系的结构性质和电子性质，论文发表在JACE上。
最初，通过将 220 个原子放入具有周期性边界条件的立方体中，在 3000 K 下生成随机液体模型。盒子的边长为14.452 Å，密度设置为2.40 g/cm3，与玻璃的实验密度一致。玻璃模型首先在 3000 K 下平衡 6000 个时间步长，时间步长为 1 fs，即6ps。然后将模型在 5 ps 内退火至 2000 K，并在 2000 K 下平衡 3 ps。随后，温度在 8.5 ps 内降至 300 K，然后在 300 K 下平衡时间为 4.5 ps。在平衡过程中3000 K 和 300 K 时，系统的能量波动在一个稳定值上，表明系统已达到稳定状态（图S1）。使用 VASP 进一步优化了 300 K 下的最终结构，没有对称约束，用于计算和分析玻璃的电子结构。

2020年重庆大学的Chenguang Bai团队000基于CASTEP模块对CaO-SiO2-Al2O3三元渣体系的电子结构和物理性质进行第一原理研究，论文发表在Journal of Non-Crystalline Solids期刊上。
（GGA, PW91, NVT, Nose， 通过模拟 3500 K 下 1 ps 时间间隔的炉渣结晶构型，获得炉渣系统的熔融结构。然后在 1773 K 下再次模拟熔渣结构 5 ps，以获得高炉渣配置。从头算分子动力学 (AIMD) 模拟的 k 点设置为布里渊区采样的伽马点。选择了超软赝势）

电池第一性原理计算

2014年印度的Swapan K. Pati团队000基于cp2k的QS模块进行了二维硼片作为锂离子电池负极材料的可能应用：DFT 和 AIMD 研究，论文发表在JMCA上。

2017年日本东京大学固体物理研究所的Seiji Kawasaki团队000基于lammps和cp2k研究了二氧化钛封端表面的固有超亲水性，论文发表在JPCC期刊上。

2019年中科大李震宇团队000基于vasp研究了LiTa2PO8中的锂离子传输，论文发表在JPCC期刊上。

2020年印度的Indranil Rudra团队000基于Gaussian09，cp2k和CASTEP进行了还原电位、铝/有机硫阴极模型以及XPS模拟等计算，论文呢题目为：Li-S电池新型有机硫正极固-固界面的第一性原理表征和实验验证（First-Principles Characterization and Experimental Validation of the Solid–Solid Interface in a Novel Organosulfur Cathode for the Li–S Battery），论文发表在ACS Applied Materials & Interfaces期刊上。
2020年美国的Md Mahbubul Islam团队000基于vasp进行了多硫化锂 (LiPS) 在原始、有缺陷和掺氧二硫化钨 (WS2)上的吸附行为。阐明了通过引入替代掺杂剂来激活 WS2 基面的策略，并量化 LiPS 在边缘位置的吸收强度。论文发表在The Journal of Physical Chemistry C期刊上。
2020年澳大利亚昆士兰大学Debra J. Searles团队000基于修改后的cp2k代码进行锂离子在锂银锭固态电解质中的扩散研究，论文发表在 npj computational materials期刊上。
2020年爱沙尼亚的Vladislav B. Ivaništšev团队000基于cp2k进行了基于密度泛函理论的分子动力学模拟的石墨烯-离子液体界面电位降的研究，论文发表在JPCC期刊上。

2021年美国的Md Mahbubul Islam团队000基于vasp进行了用于提高 Na-S 电池阴极性能的单原子催化剂：密度泛函理论 (DFT) 研究，论文发表在JPCC期刊上。论文题目为：Single-Atom Catalysts for Improved Cathode Performance in Na–S Batteries: A Density Functional Theory (DFT) Study。
2021年美国宾夕法尼亚大学的Ryan Jorn团队000基于lammps和cp2k研究了锂离子电池表面膜的离子缔合和电解质结构，论文发表在JPCC期刊上。

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部分CP2K论文

2013年美国西北太平洋国家实验室的Roger Rousseau团队000基于cp2k进行了可还原氧化物-金属簇电荷转移在催化过程中的作用：从 ab Initio 分子动力学对 Au/TiO2上 CO 氧化催化机理的新见解，论文发表在JACS上。

Paper Review

Summary of Emperical Potential

分子动力学模拟这种方法最大的缺陷在于必须知道模拟体系中每对原子之间的势参数才能够精确模拟整个体系的结构和性质，而对于复杂的多元体系，往往缺少势参数。
2019年北科大的Kejiang Li001等人讨论了CMAS(CaO-MgO-Al2O3-SiO2)体系四种势参数的可移植性问题，论文中对比了Matsui007, Kawamura3, Miyake015, Guillot011等人提出的势函数，其中Matsui最早于1994年通过拟合26种晶体的结构和体模量实验数据确定了CMAS体系的势参数，这组参数也被称为CMAS94，Matsui采用的势参数包含了库伦作用力项、范德华作用力项和排斥项。1996年，Matsui016基于CMAS94M模型计算了27种晶体的对称性、晶格参数、配位数等结构数据，与实验观测值吻合。
Kawamura《Material design using personal computer》一书中将CMAS94势参数扩展到了包含SiO2,Al2O3,CaO,MgO,K2O和Na2O在内的多组分硅酸盐晶体、液体和玻璃体系中。
Miyake通过添加Morse项来优化Matsui和Kawamura等人的势参数，使其应用于SiO2-Al2O3-CaO-MgO-K2O-Na2O六元体系。
2007年Guillot使用BMH势函数在CMAS94模型的基础上发展出了一种适用于K2O–Na2O–CaO–MgO–FeO–Fe2O3–Al2O3–TiO2–SiO2九元体系的势参数，并将该组势参数用于硅酸盐熔体在低压(011)和高压状态(018)下结构性质的模拟。
虽然上面的四种势参数都是通过对比固体的结构和性质拟合得到的，但是这些参数也被频繁的用于熔体结构和性质模拟。

2017年Mitchell Leibowitz创建的interatomic-potentials这个网站介绍了Born Mayer Huggins、Tersoff、Stilinger-Weber、Lennard Jones、Morse、Buckingham等在内的各种势函数。



年份和姓名(能够避免重复)，链接，拟合方法，体系，势函数，特征成分，研究内容，特点

1990年van Beest BW023基于实验数据和从头算分子模拟拟合了SiO2-Al2O3-P2O5体系的BMH势参数，O的原子电荷为-1.2。

1992年出版的Molecular Dynamics Simulations Proceedings of the 13th Taniguchi Symposium Kashikojima, Japan, November 6–9, 1990000一书中包含Interatomic potential models for molecular dynamics simulations of multi-component oxides一文，其中含有日本人K. Kawamura提出的多组分氧化物势模型。
1992年美国科罗拉多州立大学的A. K. Rappe等人000提出了通用力场，应用于经典分子动力学模拟，论文发表在JACS期刊上。

1997年J.M Delaye000使用BMH势函数模拟氧化物玻璃组分对结构的影响，体系为SiO2-B2O3-Na2O-Al2O3-ZrO2。

1998年俄罗斯的G. G. Boiko000基于没有色散项的BMH势函数计算了2ZnO·P2O5–2Na2O·P2O5 体系的结构。
1998年俄罗斯的David K. Belashchenko000基于没有色散项的BMH势函数计算了CaO-CaF2稀溶液体系中的MeO和MeF2, 元素包含Ca-Me-O-F(Me=Mg，Fe，Sr和 Ba)。

2001年A.N.Cormack014基于BMH势函数模拟了Na2O–CaO–SiO2玻璃体系，论文发表在JNonCrySolids。

2002年俄罗斯的D. K. Belashchenko043基于Born–Mayer势计算了CaO-P2O5体系，文中列举出了势参数。
2002年A. N. Cormack和J. Du000基于分子动力学模拟探究了Na2O–SiO2玻璃体系中碱金属离子的迁移机制，使用的参数源于(D. M.Teter, personal communication),但已经无法查到，论文发表在PCCP上，这是一篇高被引的论文。

2003年俄罗斯的G. G. Boiko029 基于BMH势函数研究了Na2O–ZnO–P2O5熔体中的离子扩散机制。
2003年日本的Yasushi Sasaki000基于简化的BMH势函数使用MXDORTHO程序研究了Na2O-NaF-SiO2熔融硅酸盐体系，论文发表在ISIJ上。
2003年A.N. Cormack000使用分子动力学模拟研究了Na2O–SiO2二元玻璃中Na离子的迁移机制。

2004年Anke Winkler和Jürgen Horbach000基于分子模拟研究了铝硅酸盐体系(Al2O3)2(SiO2) 中的结构和扩散。

2005年俄罗斯的030基于简化的BMH势函数计算了偏硅酸盐熔体中Me2O⋅SiO2(Me=Li,Na,K,Cs)的缺陷与氧扩散。
2005年日本东京大学的Won-Gap Seo000基于没有色散项的BMH势函数计算了CaO-CaF2, BaO-CaO and BaO-CaF2体系的热力学性质和相图。
2005年美国和德国的J. A. Tossell和J. Horbach000基于分子模拟和量化计算研究了(Al2O3)2(SiO2)玻璃体系中的氧团簇。

2006年Robert N. Mead000基于BMH势模拟了(CaO)x(SiO2)1-x体系玻璃的原子结构，势参数用的是2001年A.N.Cormack[014]提出的，论文发表在JPyhsChemB。
2006年Alfonso Pedone000等人发展了一种新的势，论文发表在JPCB上，其中含有大量的二元势参数。

2007年越南的Vo Van Hoang000研究了Al2O3-SiO2熔体中氧得局域环境。
2007年日本东北大学的Yasushi Sasaki026基于简化的BMH势函数使用MXDORTHO程序研究了CaO–CaF2–Na2O–SiO2熔融硅酸盐体系。
2007年日本新日铁的Keiji SHIMODA and Koji SAITO等人000基于Morse-Coulomb–Buckingham势研究了CaO–MgO–Al2O3–SiO2渣系，注意LJ势与范德华项的区别。论文发表在ISIJ上。

2008年G. Lusvardi027使用Buckingham势函数研究了Na2O-CaO-P2O5−SiO2-CaF2体系，论文发表在JPCB上，模拟使用GULP程序进行优化，DL_POLY程序进行分子动力学模拟，势参数在论文中已被直接列出且未添加高温校正项，势参数参考的是2002年Cormack引用的、经Teter提出的势参数，该论文中的含氟势参数后被Du等人在2016年的JPCC论文中使用。
2008年日本早稻田大学的Tsuyoshi Asada000基于简化的BMH势函数计算了CaO–CaF2–SiO2熔融体系的结构和性质。

2009年日本的Hiroshi Sakuma和Katsuyuki Kawamura000基于分子动力学使用其早先开发的一种势函数研究了矿物(KAl2(OH)2(AlSi3)O10) 白云母表面水的结构与动力学，论文发表在Geochimica et Cosmochimica Acta期刊上，该势函数也被其用于之后的研究。
2009年Yasushi Sasaki000基于简化的BMH势函数使用MXDORTHO程序研究了CaO–CaF2–MgO–SiO2体系中Mg和Ca原子周围F的配位数。
2009年Rodolphe Vuilleumier000分别采用从头算和经验力场模拟了K2O–Na2O–CaO–MgO–Al2O3–SiO2体系，经验力场参数源于Guillot的论文。
2009年Antonio Tilocca000发表综述“基于分子动力学模拟的生物活性玻璃结构模型”，提出了玻璃的结构模型，启发我提出熔渣体相和界面结构模型。
2009年Alfonso Pedone000基于Buckingham、Three-Body Potential、Core−Shell三种势分别模拟了CaO/MgO对磷酸盐基SiO2-Na2O-CaO-P2O5-MgO生物玻璃体系的结构影响，论文发表在JPCC上。

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2010年英国的Antonio Tilocca000在Journal of Materials Chemistry期刊上发表综述：“生物玻璃的结构、动力学和反应性模型综述”

2011年Le-Hai Kieu012发展了SiO2–B2O3–Na2O玻璃体系中的BMH势参数，首次引入了B2O3这一组元。
2011年Jincheng Du041等人基于Buckingham势函数模拟了Al2O3-P2O5-SiO2-CeO2，论文中直接列出了势参数。
2011年法国皮埃尔和玛丽·居里大学凝聚态理论物理实验室的Bertrand Guillot团队000基于分子模拟计算了硅酸盐熔体中的二氧化碳的溶解度，论文发表在Geochimica et Cosmochimica Acta期刊上。

2012年日本的Fumiya Noritake000基于分子动力学模拟研究了高压Na2O•3SiO2熔体中结构和性质间的关系，使用的势函数比较复杂。
2012年Jincheng Du038基于Buckingham势使用DL_POLY软件模拟了SiO2-Na2O-CaO-P2O5-SrO玻璃体系，并在势函数中添加了高温 排斥项，论文中列出了势参数。除此之外，作者还对玻璃体系中分子模拟的势参数发展和演变作了综述。

2013年M. Bouhadja020 等人基于从头算针对CaO–Al2O3–SiO2 (CAS)熔体提出了更加精确的BMH势参数。
2013年瑞士斯德哥尔摩大学的Kirill Okhotnikov000针对稀土铝酸盐玻璃(RE=La,Y,Lu,Sc)RE2O3–Al2O3–SiO2，开发了新的经验势场及参数，论文发表在PCCP上。
2013年G. Malavasi000基于分子动力学模拟对SiO2–CaO–P2O5体系进行了计算，论文中列出了势参数。
2013年美国的Leslie C.Dewan000结合偶极极化效应的分子动力学模拟研究了处于共晶成分的熔融LiF-ThF4的局部结构和传输特性，这种可极化的相互作用势是从第一性原理计算中参数化的。论文发表在Journal of Nuclear Materials。

2014年重庆大学的Shengfu Zhang000 基于简化的BMH势函数研究了CaO–SiO2–Al2O3–MgO–TiO2体系结构和粘度间的关系。
2014年MahaRai013使用BMH势模拟了BaO–SiO2玻璃的结构。

2015年重庆大学的Jiang Diao团队042基于BMH势函数计算了P2O5/SiO2比例对CaO-P2O5-SiO2渣体系结构的影响，论文发表在ISIJ上。
2015年重庆大学的Yanhui Liu000 基于BMH势函数研究了CaO-SiO2-MgO-Al2O3体系中Al2O3对熔渣结构的影响。
2015年重庆大学的Ting Wu021使用BMH势函数分别研究了CaO–SiO2和CaO–Al2O3体系的结构和性质，使用的势参数源于Kawamura 《Material design using personal computer》一书。
2015年重庆大学的Shengping He团队000研究了CaO-Al2O3-SiO2-CaF2熔渣体系，使用的势参数源于Kawamura: Materials Design Using Personal Computer一书，论文发表在MMTB上。
2015年Pawel Stoch022研究了加入Cs的硼硅酸盐体系玻璃，使用的是BMH势，研究体系为SiO2–B2O3–Al2O3–CaO–Na2O–Cs2O。
2015年日本的Katsuyuki KAWAMURA团队000针对Li2O-B2O3熔体/玻璃发展了一种新的势函数。
2015年Jincheng Du等人040出版Molecular Dynamics Simulations of Disordered Materials一书，书中对成核的亚稳动力学模拟、第一性原理计算方法、硅酸盐玻璃结构和性质之间的量化关系、多组分氧化物玻璃分子动力学模拟的挑战、势参数的拟合、含过渡金属氧化物玻璃结构的分子动力学模拟、玻璃表面、玻璃中的环等做了全面的介绍，在第160页有部分势参数。
2015年华东理工大学的Jia Wang000基于BMH势函数研究了熔融二元体系(Li, Na)Cl、(Li, K)Cl和(Na, K)Cl输运性质和局部结构，论文发表在Journal of Molecular Liquids上。
2015年哈工大的Fuyi Cui团队000基于lammps进行了扩散限制聚集过程中小纳米颗粒之间界面相互作用的分子动力学模拟，论文发表在Applied Surface Science期刊上。

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2016年重庆大学的Jiang Diao000基于BMH势函数研究了CaO–SiO2–P2O5–FeO体系的结构和性质，势参数源于Material Design Using Personal Computer。
2016年重庆大学的Ting Wu010使用LJ势和BMH势模拟了CaO-SiO2-Al2O3-FeO熔渣体系的结构和粘度。
2016年重庆大学的Ting Wu000 使用BMH势函数分别研究了 Na2O–Al2O3, K2O–Al2O3, MgO–Al2O3, and CaO–Al2O3， Al2O3基二元体系的结构和性质，使用的势参数源于Kawamura 《Material design using personal computer》一书。
2016年美国的Jincheng Du团队033基于Buckingham势拟合了Na2O-Al2O3-B2O3-SiO2硼铝硅酸盐体系的势参数，论文发表在Journal of Non-Crystalline Solids期刊上。
2016年Lehlohonolo Mongalo000 使用BMH+Morse势研究了CaO–MgO–Al2O3–SiO2体系的熔体结构和导电性。
2016年意大利的Alfonso Pedone000在Biocompatible Glasses期刊上发表综述：“从生物玻璃得分子模拟中我们可以学到什么？”
2016年Jincheng Du团队000在JPCC上发文“氟硅酸盐玻璃从相分离到纳米化：高发光微晶玻璃的结构设计”，基于Buckingham+高温排斥项研究了氧氟硅酸盐SiO2-Al2O3-BaO-BaF2玻璃体系，势参数和校正参数并未直接列出，而是给出了参考文献，如势参数源于Du在2002年、2004年和论文数据以及2008年G. Lusvardi[027]发表的势参数。
2016年湖南大学的Hui Luo000基于Born–Mayer势函数使用DL_POLY软件进行了液态氟化锂扩散和粘度的分子动力学模拟，论文发表在Computational Materials Science期刊上。
2016年美国罗格斯大学的Stephen H. Garofalini000基于BMH势函数研究了SiO2-Al2O3-B2O3-CaO-Na2O钠钙铝硼硅酸盐玻璃中网络改性剂与网络形成剂的局部结构，论文发表在Journal of the American Ceramic Society期刊上。

2017年重庆大学的Xiao-Ping Liang000基于简化的BMH势函数计算了B2O3/SiO2比例对CaO–SiO2–B2O3体系结构和性质的影响，是一篇会议论文。
2017年北京化工大学的Yangxu Hu000基于分子动力学模拟了液液界面的萃取过程，并修正了双膜理论，论文发表在化工顶刊AIChE上。
2017年Jincheng Du000基于分子动力学模拟研究了硼硅酸钠和硼铝硅酸盐核废料玻璃的体积、表面结构和性能。
2017年Jincheng Du团队037基于Buckingham势模拟了B2O3-SiO2-Na2O-CaO-SrO-P2O5体系玻璃，论文发表在Journal of Materials Science，使用的模拟软件为DL_POLY,注意同一套势参数应用于DL_POLY、Lammps等不同模拟软件时单位、格式等写法上的区别。注意到该论文中提到为了解决高温问题（当两个原子在高温下非常接近时，势能值可能迅速下降到负无穷大），加入了排斥项（V（r）=Brn+Dr2）来修正。 在初期的模拟中暂不考虑该问题。论文中还提到对于B-O相互作用，Aij的值随着成分而变化，论文中针对三个组分分别取了三个值。部分势参数来源于2012年Jincheng Du038SiO2-Na2O-CaO-P2O5-SrO体系。
2017年中山大学的Weilong Wang000团队基于BMH势函数进行了”聚光太阳能用二元碱金属氯化物局部结构和输运特性的理论预测”，论文发表在Nano Energy期刊上。
2017年华东理工大学的Jia Wang000基于BMH势函数研究了四种二元系统LiCl-RbCl、LiCl-CsCl、NaCl-RbCl和NaCl-CsCl熔融碱金属氯化物输运性质和局部结构，论文发表在Journal of Molecular Liquids期刊上。
2017年上海交通大学Jun Wang团队000基于BMH势函数研究了熔融 NaCl-KCl-LiCl 混合物物理性质和局部结构的温度和浓度依赖性，论文发表在Journal of Molecular Liquids期刊上。
2017年北科大Kejiang Li团队000基于Miyake势研究了碱化过程中影响焦灰流动性的因素，论文发表在Chemical Engineering Journal期刊上。
2017年北科大Kejiang Li团队000基于Miyake势进行了高炉高温区焦灰行为的分子动力学研究：碱的影响，论文发表在Energy & Fuels。
2017年南方科技大学的张作泰团队000基于使用Buckingham势的分子模拟结合X射线光电子能谱和拉曼光谱技术进行了CaO-SiO2-P2O5三元玻璃中磷的结构研究，论文发表在MMTB上。
2017年英国的Gavin Mountjoy团队000基于GULP软件模拟CaO-SiO2-CaCl2含氯硅酸盐玻璃中相分离的开始，论文发表在JPCB上。

2018年北科ChunheJiang团队000基于Coulomb–Buckingham势研究了MgO/Al2O3比例对SiO2-Al2O3-CaO-MgO熔渣结构和性质的影响，论文发表在Journal of Non-Crystalline Solids期刊上。
2018年北科Kejiang Li团队000基于CMAS94模型参数进行了CaO(MgO)对硅铝酸盐体系结构和性能的分子动力学模拟研究，论文发表在Journal of Molecular Liquids期刊上。
2018年Jincheng Du000团队基于分子动力学模拟研究了B2O3/SiO2取代对Na2O-CaO-SrO-P2O5-SiO2生物活性玻璃结构和性能的影响，论文发表在PCCP上，论文中并未直接列出势参数，参考的是2017年Du[037]发表在Journal of Materials Science上的势参数。
2018年Jincheng Du032团队针对Li2O, K2O, P2O5, Al2O3, CaO, MgO, SrO硅酸盐玻璃体系发展除了新的BMH势参数，该论文发表JAmCeramSoc后又进行了更正，注意更正。
2018年Mengyi Wang017在Guilliot提出的K2O–Na2O–CaO–MgO–FeO–Fe2O3–Al2O3–TiO2–SiO2体系BMH势参数的基础上拟合了B2O3这一组元势参数，并重点考察了Na2O–CaO–SiO2-B2O3这一四元玻璃体系的结构和性质。
2018年Alfonso Pedone046在JPCB上发表论文“含卤化物磷硅酸盐生物活性玻璃的分子动力学研究”，研究的体系为SiO2-CaO-P2O5-CaF2-CaCl2，作者采用了Buckingham势、Three body势和core−shell spring势。
2018年瑞典斯德哥尔摩大学Mattias Edén团队000基于改进的B-O和P-O力场通过DLPOLY4.08软件对Na2O–CaO–B2O3–SiO2–P2O5多组分硼硅酸盐玻璃体系进行分子动力学模拟，论文发表在PCCP上。

2019年重庆大学的Shengping He(第一兼通讯)025在使用BMH势函数研究了CaO-SiO2-CaF2渣体系的结构和性质，势函数来源为Kawamura 《Material design using personal computer》，论文发表在MMTB上。
2019年韩国的Hyunsik Park团队000利用分子动力学模拟估算节能钛铁矿冶炼过程中TiO2-FeO-Na2O渣粘度，使用的是经Guillot提出的BMH势参数，论文发表在scientific reports上。
2019年巴西的Maziar Montazerian035等人在International Materials Reviews期刊上发表综述“生物活性玻璃的模型驱动设计：从分子动力学到机器学习”
2019年法国的Mohammed Bouhadja000基于BMH势函数研究了(Al2O3)x-(SiO2)1−x (AS)二元铝硅酸盐熔体的动力学性质，论文发表在Journal of Physics: Condensed Matter期刊上。
2019年Mathieu Bauchy团队036基于分子动力学模拟研究了硼硅酸盐玻璃中改性剂的聚集和回避原理，论文发表在The Journal of Chemical Physics。
2019年Jincheng Du团队000基于分子动力学模拟研究了氟铝硅酸盐玻璃体系的相分离结构模型，论文模拟的体系为SiO2-Al2O3-BaF2氟铝酸盐玻璃体系，该论文发表在J.Eur.Ceram.Soc.期刊上，该工作使用DL_POLY进行模拟，使用的势参数为Buckingham势并添加了高温校正项，论文并未直接列出势参数，而是参考了Du在2002年、2004年提出的势参数，以及其在2015年出版的书Molecular Dynamics Simulations of Disordered Materials中。
2019年Jincheng Du团队000在JPCB上发文“相分离SiO2–Al2O3–RF3–NaF玻璃中RF3/NaRF4纳米晶沉淀的结构起源：分子动力学模拟研究”，论文中并未直接列出使用的势函数和势参数。
2019年Jincheng Du团队000在Advanced Theory and Simulation上发文“相分离氟铝硅酸盐玻璃形成 BaF2/Ba1−xRxF2 +x/RF3 纳米晶体的结构起源：分子动力学模拟研究”，论文中并未直接列出势函数和势参数。
2019年北科大的Kejiang Li001等人讨论了CMAS(CaO-MgO-Al2O3-SiO2)体系四种势参数的可移植性问题，论文中对比了Matsui, Kawamura, Miyake, Guillot等人提出的势函数。
2019年瑞典乌普萨拉大学的David van der Spoel团队000基于GROMACS进行LiCl-KCl混合物熔盐中结构、动力学和热力学之间的直接联系研究，论文发表在JPCC期刊上。
2019年德国Sudheer Ganisetti等人000基于Miyake和Pedone等人提出的势函数基于分子动力学模拟阐明碱土铝硅酸盐玻璃中 Al-NBO 键、Al-O-Al 键和簇的形成，论文发表在PCCP上。

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2020年加拿大西安大略大学地球科学系的H. Wayne Nesbitt团队000进行了亲核取代反应机制：硅酸盐熔体中化学形态和传输特性的原子分子视角，论文发表在Chemical Geology期刊上。这篇论文比较重要，因为开始从原子尺度上讨论硅酸盐熔体中的反应。
2020年安徽工业大学的Shama Sadaf024基于BMH势函数研究了B2O3对CaO–SiO2-B2O3体系结构和粘度的影响，论文发表在steel research上,势参数源于Materials Design using Personal Computer一书。
2020年西安建筑科技大学的Jiantao Ju(一作兼通讯)000通过实验研究了CaF2–CaO–Al2O3–MgO–TiO2–(Li2O)电渣体系中氟化物的挥发，论文发表在scientific reports上。
2020年东北大学的Xiaobo Zhang028基于Buckingham势函数研究了CaO–SiO2–CaF2和CaO–Al2O3–CaF2体系中氟对熔体结构的影响，论文发表在ISIJ上，论文中未对势参数的来源进行说明。
2020年Charlie Ma000基于BMH+Morse势采用多元分析计算了CaO-K2O-SiO2体系中结构和粘度之间的关系，势参数用的是Miyake发展的，论文发表在Chemical Engineering Science上。
2020年越南的P.K. Hung000使用分子动力学模拟研究了低钠Na2O.4SiO2硅酸盐体系中Na的跳跃扩散机制，使用的势函数是其之前提出的一种不常用的势，该论文发表在Journal of Molecular Liquids上。
2020年法国的Noël Jakse团队000基于BMH势函数研究了CaO的结构和热力学性质，论文发表在The Journal of Chemical Physics期刊上。
2020年浙江大学的Liu Yong团队000基于BMH势函数研究了Na2O–Al2O3–SiO2体系的离子自扩散。
2020年宾大的John C. Mauro团队000评价了经典势函数应用于硼硅酸盐玻璃的分子动力学模拟，论文发表在Journal of Non-Crystalline Solids期刊上。
2020年Jincheng Du团队000基于BMH势进行钙铝硅酸盐玻璃定量构效关系分析。
2020年Jincheng Du团队039对含氟硅酸铝低活性核废料玻璃结构进行了分子动力学模拟研究，在这篇论文中，作者提到由于势函数的限制，分子模拟以往很少在在含氟玻璃上进行，但随着混合阴离子体系的发展，MD模拟方法将研究扩展到氟硅酸盐玻璃，并证明它能够捕捉均匀混合阴离子玻璃和纳米氟化物相分离玻璃的结构特征。论文中并未直接列出势参数，而是参考其在2016年和在2019年分别发表在JPCC和J.Eur.Ceram.Soc论文中的势参数，而其2016年的数据的数据实则是2008年G. Lusvardi[027]发表的势参数。
2020年比利时鲁汶大学的Christina Siakati000基于Buckingham势揭示玻璃状CaO-FeO-SiO2渣的纳米结构，论文发表在Journal of Non-Crystalline Solids。
2020年美国的Siddharth Sundararaman等人048针对含有两种网络形成体的硼铝硅酸盐玻璃体系开发了新的势参数，论文发表在The Journal of Chemical Physics（JCP）期刊上。
2020年荷兰的Sergio E. Ruiz-Hernandez000基于DL_POLY和cp2k进行了水扩散到生物活性磷酸盐基玻璃表面对其溶解行为影响的分子动力学研究，论文发表在Journal of Non-Crystalline Solids期刊上。

2021年Young jae Kim019基于BMH势函数模拟了MgF2-LiF-MgO体系的粘度和结构，论文中引用的势参数是参考以往文献组合起来的。
2021年北科Zhisheng Bi049基于BMH势函数研究了B2O3-SiO2-CaO-Al2O3体系中B2O3对熔渣结构的影响，论文对势参数的来源并未进行说明。
2021年北科Zhisheng Bi000基于BMH势函数分别研究了SiO2-CaO-Al2O3和SiO2-CaO-B2O3体系中Al2O3和B2O3对SiO2-CaO基熔体结构和性质的影响。
2021年Jincheng Du团队031发展了SiO2-Na2O-B2O3–Al2O3硼酸盐体系的Buckingham势参数，主要参考对比的是2011年的Kieu[012]势，2016年Du[033]势，2018年的Bauchy[017]势以及2018年的Du[032]势，这些势主要都是用来模拟硼硅酸盐玻璃的。
2021年山西煤化所的Longfei Gao000基于BMH势函数和环统计研究了不同CaO/Na2O比例下SiO2–Al2O3–CaO–Na2O煤灰渣的结构和流动性。
2021年日本的Fumiya Noritake000基于分子动力学模拟并使用一个复杂的势函数研究了硅酸盐体系中网络形成体元素的扩散机制，研究的体系为Na2O·SiO2。
2021年越南的To Ba Van000基于BMH势函数研究了SiO2-Al2O3液相体系中畴结构、微观偏析和动力学不均匀性。
2021年Marco Bertani034发展修正了多组分氧化物玻璃体系中PMMCS力场参数，论文发表在美国物理学会的PHYSICAL REVIEW MATERIALS期刊上。
2021年浙江大学Yong Liu团队047基于分子动力学模拟修正了P2O5-Na2O-Al2O3-SiO2玻璃体系的随机网络模型，论文发表在RSC Advances上。
2021年重庆大学的Zhe Wang团队000通过将分子动力学模拟和机器学习结合起来，研究了CaO-SiO2-Al2O3三元渣系结构和性质间的关系，并进行了热导率预测，论文发表在Journal of Molecular Liquids期刊上。
2021年北科Kejiang Li团队000基于分子动力学模拟研究了CaO和FeO对硅铝酸盐体系结构和性能的影响，研究体系为SiO2-Al2O3-CaO(FeO) ，使用的是函数为Buckingham势，论文发表在Journal of Molecular Liquids期刊上。
2021年北科Kejiang Li团队000基于分子动力学模拟研究了不同碱度条件下MnO含量对渣结构和性质的影响，研究体系为CaO-SiO2-Al2O3-MnO，使用了Buckingham力场和LJ力场，势参数源于CMAS94和UFF通用力场参数，论文发表在Journal of Molecular Liquids期刊上。
2021年江西理工大学的Helin Fan等人000基于Buckingham力场研究了TiO2-SiO2-MgO-CaO体系中结构和传输性质间的关系，论文发表在Journal of Molecular Liquids期刊上。
2021年北科Kexin Jiao团队000采用COMPASS力场研究了碳含量和温度对铁水粘度的影响，论文发表在Journal of Molecular Liquids期刊上。
2021年生态环境部南京环境科学研究所Hongling Zhang团队000采用Materials Studio软件Forcite 模块中的UFF力场进行MD模拟，结合Gaussian09中B3lyp泛函+6-31G*基组进行结构优化，进行了苏氨酸-蒙脱石复合物与Pb2+或Cu2+的相互作用及机理研究，论文发表在Journal of Molecular Liquids期刊上。
2021年挪威科技大学的Jafar Safarian团队000基于lammps模拟和实验研究了La2O3添加到CaO-SiO2渣中的影响：Si-Sn合金的结构演化和杂质分离，即含稀土熔渣精炼除B、P，论文发表在MMTB上。
2021年重庆大学Shuheng Huang团队000基于Buckingham势函数研究了CaO-SiO2-Al2O3-Na2O-F (CSANF)体系，全面了解硅酸盐熔体中氟的微观结构和挥发机理，论文通过通过PLS分析，建立了氟的微观结构与扩散系数之间的关系。势参数源于Kawamura 《Material design using personal computer》，论文发表在Chemical Engineering Science期刊上。
2021年北科Kejiang Li团队000基于BMH势函数研究了SiO2–CaO–Al2O3–B2O3体系中两性氧化物（Al2O3和B2O3）的酸性向碱性转变机制：分子动力学研究，所用的势参数是重庆大学两篇文章中拼凑起来的，论文发表在Ceramics International期刊上。
2021年俄罗斯科学院乌拉尔分院固体化学研究所的Ilya S. Popov团队000基于分子动力学模拟研究了硫化镉纳米颗粒和硅酸盐玻璃复合材料内的相平衡：原子观，论文发表在Computational Materials Science期刊上。
2021年江西理工大学的Helin Fan团队000采用lammps软件基于BMH势函数研究了B2O3对钛渣熔体结构和性能的影响。论文发表在Journal of Materials Research and Technology期刊上。

分子动力学模拟Si熔体及相关合金熔体

2018年清华大学的黄秀松000开发了新的势函数用于Si熔体的lammps分子动力学模拟，论文发表在Computational Materials Science期刊上。

2019年清华大学的黄秀松000基于lammp分子动力学模拟研究了Al-Si合金熔体的液态结构，论文发表在Journal of Non-Crystalline Solids期刊上。

2021年深圳大学的黄秀松000使用lammps基于新开发的势函数对HfNbTaZr高熵合金化学短程有序进行了原子模拟，论文发表在Materials & Design期刊上。

000
000


团队000
等人000

北京科技大学

2017年北科大Kejiang Li团队000基于Miyake势研究了碱化过程中影响焦灰流动性的因素，论文发表在Chemical Engineering Journal期刊上。
2017年北科大Kejiang Li团队000基于Miyake势进行了高炉高温区焦灰行为的分子动力学研究：碱的影响，论文发表在Energy & Fuels。

2018年北科Kejiang Li团队000基于CMAS94模型参数进行了CaO(MgO)对硅铝酸盐体系结构和性能的分子动力学模拟研究，论文发表在Journal of Molecular Liquids期刊上。

2019年北科大的Kejiang Li001等人讨论了CMAS(CaO-MgO-Al2O3-SiO2)体系四种势参数的可移植性问题，论文中对比了Matsui, Kawamura, Miyake, Guillot等人提出的势函数。

2021年北科Zhisheng Bi000基于BMH势函数研究了B2O3-SiO2-CaO-Al2O3体系中B2O3对熔渣结构的影响，论文对势参数的来源并未进行说明。
2021年北科Zhisheng Bi000基于BMH势函数分别研究了SiO2-CaO-Al2O3和SiO2-CaO-B2O3体系中Al2O3和B2O3对SiO2-CaO基熔体结构和性质的影响。
2021年北科Kejiang Li团队000基于分子动力学模拟研究了CaO和FeO对硅铝酸盐体系结构和性能的影响，研究体系为SiO2-Al2O3-CaO(FeO) ，使用的是函数为Buckingham势，论文发表在Journal of Molecular Liquids期刊上。
2021年北科Kejiang Li团队000基于分子动力学模拟研究了不同碱度条件下MnO含量对渣结构和性质的影响，研究体系为CaO-SiO2-Al2O3-MnO，使用了Buckingham力场和LJ力场，势参数源于CMAS94和UFF通用力场参数，论文发表在Journal of Molecular Liquids期刊上。
2021年北科Kexin Jiao团队000采用COMPASS力场研究了碳含量和温度对铁水粘度的影响，论文发表在Journal of Molecular Liquids期刊上。
2021年北科Kejiang Li团队000基于BMH势函数研究了SiO2–CaO–Al2O3–B2O3体系中两性氧化物（Al2O3和B2O3）的酸性向碱性转变机制：分子动力学研究，所用的势参数是重庆大学两篇文章中拼凑起来的，论文发表在Ceramics International期刊上。

重庆大学

2014年重庆大学的Shengfu Zhang000 基于简化的BMH势函数研究了CaO–SiO2–Al2O3–MgO–TiO2体系结构和粘度间的关系。

2015年重庆大学的Yanhui Liu000 基于BMH势函数研究了CaO-SiO2-MgO-Al2O3体系中Al2O3对熔渣结构的影响。
2015年重庆大学的Ting Wu021使用BMH势函数分别研究了CaO–SiO2和CaO–Al2O3体系的结构和性质，使用的势参数源于Kawamura 《Material design using personal computer》一书。
2015年重庆大学的Shengping He团队000研究了CaO-Al2O3-SiO2-CaF2熔渣体系，使用的势参数源于Kawamura: Materials Design Using Personal Computer一书。
2015年重庆大学的Jiang Diao团队042基于BMH势函数计算了P2O5/SiO2比例对CaO-P2O5-SiO2渣体系结构的影响，论文发表在ISIJ上。其中CaO-SiO2的参数源于Material Design Using Personal Computer一书，P2O5的参数源于另外一篇文献2002[043]。

2016年重庆大学的Jiang Diao000基于BMH势函数研究了CaO–SiO2–P2O5–FeO体系的结构和性质，势参数源于Material Design Using Personal Computer。
2016年重庆大学的Ting Wu010使用LJ势和BMH势模拟了CaO-SiO2-Al2O3-FeO熔渣体系的结构和粘度。
2016年重庆大学的Ting Wu000 使用BMH势函数分别研究了 Na2O–Al2O3, K2O–Al2O3, MgO–Al2O3, and CaO–Al2O3， Al2O3基二元体系的结构和性质，使用的势参数源于Kawamura 《Material design using personal computer》一书。

2017年重庆大学的Xiao-Ping Liang000基于简化的BMH势函数计算了B2O3/SiO2比例对CaO–SiO2–B2O3体系结构和性质的影响，是一篇会议论文。

2019年重庆大学的Shengping He(第一兼通讯)025在使用BMH势函数研究了CaO-SiO2-CaF2渣体系的结构和性质，势函数来源为Kawamura 《Material design using personal computer》，论文发表在MMTB上。

2021年重庆大学Shuheng Huang团队000基于Buckingham势函数研究了CaO-SiO2-Al2O3-Na2O-F (CSANF)体系，全面了解硅酸盐熔体中氟的微观结构和挥发机理，论文通过通过PLS分析，建立了氟的微观结构与扩散系数之间的关系。势参数源于Kawamura 《Material design using personal computer》，论文发表在Chemical Engineering Science期刊上。

Journal of Molecular Liquids期刊分子模拟论文

2017年上海交通大学Jun Wang团队000基于BMH势函数研究了熔融 NaCl-KCl-LiCl 混合物物理性质和局部结构的温度和浓度依赖性，论文发表在Journal of Molecular Liquids期刊上。

2018年北科Kejiang Li团队000基于CMAS94模型参数进行了CaO(MgO)对硅铝酸盐体系结构和性能的分子动力学模拟研究，论文发表在Journal of Molecular Liquids期刊上。

2021年重庆大学的Zhe Wang团队000通过将分子动力学模拟和机器学习结合起来，研究了CaO-SiO2-Al2O3三元渣系结构和性质间的关系，并进行了热导率预测，论文发表在Journal of Molecular Liquids期刊上。
2021年北科Kejiang Li团队000基于分子动力学模拟研究了CaO和FeO对硅铝酸盐体系结构和性能的影响，研究体系为SiO2-Al2O3-CaO(FeO) ，使用的是函数为Buckingham势，论文发表在Journal of Molecular Liquids期刊上。
2021年北科Kejiang Li团队000基于分子动力学模拟研究了不同碱度条件下MnO含量对渣结构和性质的影响，研究体系为CaO-SiO2-Al2O3-MnO，使用了Buckingham力场和LJ力场，势参数源于CMAS94和UFF通用力场参数，论文发表在Journal of Molecular Liquids期刊上。
2021年江西理工大学的Helin Fan等人000基于Buckingham力场研究了TiO2-SiO2-MgO-CaO体系中结构和传输性质间的关系，论文发表在Journal of Molecular Liquids期刊上。
2021年北科Kexin Jiao团队000采用COMPASS力场研究了碳含量和温度对铁水粘度的影响，论文发表在Journal of Molecular Liquids期刊上。
2021年生态环境部南京环境科学研究所Hongling Zhang团队000采用Materials Studio软件Forcite 模块中的UFF力场进行MD模拟，结合Gaussian09中B3lyp泛函+6-31G*基组进行结构优化，进行了苏氨酸-蒙脱石复合物与Pb2+或Cu2+的相互作用及机理研究，论文发表在Journal of Molecular Liquids期刊上。

总结

在使用这些参数之前注意对比这些参数的单位与以往的是否一致。

2017年Jincheng Du团队037在氧化硼取代对含SrO生物活性玻璃结构和生物活性的影响一文中，基于Buckingham势模拟了B2O3-SiO2-Na2O-CaO-SrO-P2O5体系玻璃，该体系组分距离目标组分只差CaF2，势函数中还添加了排斥项来修正高温下的问题（当两个原子在高温下非常接近时，势能值可能迅速下降到负无穷大），高温问题在许多论文中都未考虑，其势参数为
Table 1 The atomic charges and the Buckingham potential parameters: A ij , ρij, Cij (B–O Aij is for the 25B composition)
Pairs A ij (eV) ρ ij (Å) C ij (eV·Å6)
Si2.4–O−1.2 13702.905 0.193817 54.681
P3.0–O−1.2 26655.472 0.181968 86.856
B1.8–O−1.2 12962.4964 0.1240 35.0019
O−1.2–O−1.2 2029.2204 0.343645 192.58
Na0.6–O−1.2 4383.7555 0.243838 30.70
Ca1.2–O−1.2 7747.1834 0.252623 93.109
Sr1.2–O−1.2 14566.637 0.245015 81.773

2012年Jincheng Du038基于Buckingham势使用DL_POLY软件模拟了SiO2-Na2O-CaO-P2O5-SrO玻璃体系，并在势函数中添加了高温 排斥项。2017年的势参数是在2012年的基础上发展出来的，现在需要做的就是基于谷歌学术被引文献和Semantic学术查找在此基础上是否有CaF2的势参数。
Table 1. Buckingham potential parameters.
Pairs A (eV) ρ (Å) C (eV.Å6)
Si2.4―O− 1.2 13702.905 0.193817 54.681
P3.0―O− 1.2 26655.472 0.181968 86.856
O− 1.2―O− 1.2 2029.2204 0.343645 192.58
Na0.6―O− 1.2 4383.7555 0.243838 30.70
Ca1.2―O− 1.2 7747.1834 0.252623 93.109
Sr1.2―O− 1.2 14566.637 0.245015 81.773

2008年G. Lusvardi027使用Buckingham势函数研究了Na2O-CaO-P2O5−SiO2-CaF2体系，论文发表在JPCB上，模拟使用GULP程序进行优化，DL_POLY程序进行分子动力学模拟，势参数在论文中已被直接列出且未添加高温校正项，势参数参考的是2002年Cormack引用的、经Teter提出的势参数，该论文中的含氟势参数后被Du等人在2016年的JPCC论文中使用。
Table 2. Potential Parameters used for Molecular Dynamics Simulations
Aij [eV] ρij [Å] Cij [eV·Å6]
Si+2.4−O−1.2 13702.905 0.193817 54.681
P+3.0−O−1.2 26655.472 0.181968 86.856
Na+0.6−O−1.2 4383.75555 0.243838 30.700
Ca+1.2−O−1.2 7747.1834 0.252623 93.109
O−1.2−O−1.2 1844.7458 0.343645 192.58
Si+2.4−F−0.6 53193.487 0.146851 5.0196
Na+0.6−F−0.6 58286.140 0.169113 4.1555
Ca+1.2−F−0.6 976421.09 0.147304 12.163
F−0.6−F−0.6 11510.594 0.225005 29.257
O−1.2−F−0.6 1863.6049 0.328812 141.27

2016年Jincheng Du团队000在JPCC上发文“氟硅酸盐玻璃从相分离到纳米化：高发光微晶玻璃的结构设计”，基于Buckingham+高温排斥项研究了氧氟硅酸盐SiO2-Al2O3-BaO-BaF2玻璃体系，势参数和校正参数并未直接列出，而是给出了参考文献，如势参数源于Du在2002年、2004年论文数据以及2008年G. Lusvardi[027]发表的势参数。

2020年Jincheng Du团队039对含氟硅酸铝低活性核废料玻璃结构进行了分子动力学模拟研究，在这篇论文中，作者提到由于势函数的限制，分子模拟以往很少在在含氟玻璃上进行，但随着混合阴离子体系的发展，MD模拟方法将研究扩展到氟硅酸盐玻璃，并证明它能够捕捉均匀混合阴离子玻璃和纳米氟化物相分离玻璃的结构特征。论文中并未直接列出势参数，而是参考其在2016年和在2019年分别发表在JPCC和J.Eur.Ceram.Soc论文中的势参数。

2015年Jincheng Du等人040出版Molecular Dynamics Simulations of Disordered Materials一书，书中对成核的亚稳动力学模拟、第一性原理计算方法、硅酸盐玻璃结构和性质之间的量化关系、多组分氧化物玻璃分子动力学模拟的挑战、势参数的拟合、含过渡金属氧化物玻璃结构的分子动力学模拟、玻璃表面、玻璃中的环等内容做了全面的介绍。下面是其第七章中的部分势参数，更多参数参见其参考文献。
Table 7.1 Atomic charge and Buckingham potential parameters for oxide glasses [11–21]
Pairs A (eV) ρ (Å) C (eVÅ6)
O−1.2–O−1.2 2029.2204 0.343645 192.58
Si2.4–O−1.2 13702.905 0.193817 54.681
P3.0–O−1.2 26655.472 0.181968 86.856
Al1.8–O−1.2 12201.417 0.195628 31.997
Li0.6–O−1.2 41051.938 0.151160 0.0
Na0.6–O−1.2 4383.7555 0.243838 30.70
K0.6–O−1.2 20526.972 0.233708 51.489
Ca1.2–O−1.2 7747.1834 0.252623 93.109
Sr1.2–O−1.2 14566.637 0.245015 81.773
Y1.8–O−1.2 29526.977 0.211377 50.477
La1.8–O−1.2 4369.39 0.2786 60.28
Er1.8–O−1.2 58934.851 0.195478 47.651
Eu1.8–O−1.2 5950.5287 0.253669 27.818
Ce1.8–O−1.2 11476.9522 0.242032 46.7604
Ce2.4–O−1.2 31697.724 0.21836 90.659

2020年东北大学的Xiaobo Zhang028基于Buckingham势函数研究了CaO–SiO2–CaF2和CaO–Al2O3–CaF2体系中氟对熔体结构的影响，论文发表在ISIJ上，论文中未对势参数的来源进行说明。
Table 1. Buckingham potential parameters of particle pairs in this study.
Atom 1 Atom 2 Aij/(eV) ρij/(Å) Cij/(eV·Å6)
Si Si 4 142.15 0.16 0
Si Ca 26 674.68 0.16 0
Si O 62 794.37 0.165 0
Si F 43 406.00 0.165 0
Ca Ca 329 051.6 0.16 4.355
Ca O 717 827.0 0.165 8.67
Ca F 496 191.5 0.165 8.67
O O 1 497 049.0 0.17 17.34
O F 1 046 135.4 0.17 17.34
F F 730 722.8 0.17 17.34
Al Al 4 142.149 0.16 0.0
Al Ca 36 918.57 0.16 0.0
Al O 86 057.58 0.165 0.0
Al F 59 481.584 0.165 0.0

2019年重庆大学的Shengping He(第一兼通讯)025在使用BMH势函数研究了CaO-SiO2-CaF2渣体系的结构和性质，势函数来源为Kawamura 《Material design using personal computer》，论文发表在MMTB上。
Table I Parameters for Born–Mayer–Huggins (BMH) Potential in the CaO-SiO2-CaF2 System
From: Molecular Dynamics Simulation of the Structure and Properties of CaO-SiO2-CaF2 Slag Systems

Atom1 Atom2 Aij(eV) Bij(1/Å) Cij(eV·Å6)
O O 1497693.5 5.88 17.34
O Ca 718136 6.06 8.67
O F 1046135.4 5.88 17.34
O Si 62821.4 6.06 0
Ca Ca 329193.3 6.25 4.34
Ca F 496191.5 6.06 8.67
Ca Si 26686.2 6.25 0
F F 730722.8 5.88 17.34
F Si 43406 6.06 0
Si Si 2163.3 6.25 0

2015年重庆大学的Jiang Diao团队042基于BMH势函数计算了P2O5/SiO2比例对CaO-P2O5-SiO2渣体系结构的影响，论文发表在ISIJ上。
Atom1 Atom2 Aij(eV) Bij(1/¡) Cij(eV·¡6) Atom1 Atom2 Aij(eV) Bij(1/¡) Cij(eV·¡6)
Ca Ca 329171.5 6.3 4.3 Si P 1081.6 12.5 0
Ca Si 26684.4 6.3 0 Si O 62817.2 6.1 0
Ca P 164585.8 12.5 0 P P 0 0.0 0
Ca O 718088.6 6.1 8.7 P O 1847.7 3.5 0
Si Si 2163.2 6.3 0 O O 1497594.3 5.9 17.4

专业名词

Markdown语法制表记得表头首末空行

Forcefield 力场，High-Throughput Molecular Dynamics Simulations 高通量分子动力学模拟 strontium 锶 fluorine 氟
machine learning 机器学习 potential parameters 势参数 bioactive glass生物玻璃 ilmenite 钛铁矿


email

email reply

  1. 祝福/问候
    BR
    Best Regards
    I hope you enjoyed your weekend
    Hope you had a wonderful vacation
    First and most important of all, I wish every one of you a safe and healthy new fiscal year.

  2. 结尾用语
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  3. 结尾/期待回复
    Looking forward to your updates
    If you could back to me by end of next Wednesday would be great.
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    Could you help to reply the email I sent you before for the next step about the CMS
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    We must do all we can now to protect jobs as much as possible through this period of
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  4. 转发邮件
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    Sync up sales’ suggestion for your information.
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  5. 收到/回复
    Really appreciate your prompt reply! And Thanks for your detailed information !
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  6. 感谢用语
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  7. 致歉用语
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  8. 附件用语
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  9. 会议邀请
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  10. 会议纪要
    Below are the notes taken from the meeting. Should you see any discrepancy, feel free to
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    Thank you for making time on a very busy Friday afternoon to join our discussion. I hope
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  11. 询问用语
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  12. 事物状态

  13. In progress: in UK’s implementation plan

  14. To investigate: to check the feasibility of updates or options.

  15. Deferred: will not in recent plan and action list for now.
    Correction to previous notice please find below latest update as per the incident
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  16. 提醒用语
    Just a kinder reminder that we will conduct the rollback tomorrow. I will send a further
    notice before implementation starts.
    Since you haven’t raised any concern on starting time of 6:00 pm yet, I suppose we got
    green light on it. Should you have any concern, please let me know beforehand.
    u should pick this up seriously, language would be part of your presentation skills
    which can let people aware and understand your capability/ skills
    good that you have this awareness and really put in actions
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    keep practicing, reading loud, speaking out & listening……
    Root cause analysis is ongoing.
    The battleground for customer loyalty has moved from the tangible to the intangible.
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    Rest assured we will do our best to make this happen if it is safe to do so.
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Please do not hesitate to contact me if you require any further information. Yours sincerely,I look forward to your reply.
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Dear Eloise,

Thank you very much for offering your assistance on the upcoming Human Resources project. I really appreciate your willingness to help out outside your current position.

It is helpful to have someone who has had experience with similar issues on previous projects to offer guidance and direction. I know HR is happy to have you assisting in this matter.

Let me know if you need anything from me. I can make someone available to help your team out while you spend a few days with the Human Resources staff.

Regards,

Peter

Dear Eloise,

Thank you very much for offering your assistance on the upcoming Human Resources project. I really appreciate your willingness to help out outside your current position.

It is helpful to have someone who has had experience with similar issues on previous projects to offer guidance and direction. I know HR is happy to have you assisting in this matter.

Let me know if you need anything from me. I can make someone available to help your team out while you spend a few days with the Human Resources staff.

Regards,

Peter

Dear Sophie,

Thank you so much for coming in all those extra hours last week to help me get the new shop set up. I can’t believe how much more space we have for display, and the new kitchen is going to make it so much easier to meet the demand for wedding cakes this summer!

You are always such a help, and I appreciate your support in every way. I am so happy to have you as my assistant, and I’m looking forward to working with you as we move into the next phase with our growing business.

Fond regards,

Melissa

stellanick@byu.edu

Dr. Stella Nickerson
Dear Prof.
l am a graduate student from … university in China.
My research is focused on machine learning and online learning. I have recently read your paper “Parsimonious OnlineLearning with Kernels via Sparse Projections in FunctionSpace”, which appeared in”web of science”. I am very interested in this article and have studied it carefully. I try to validate your simulation results about POLK show in Fig.7 and Fig.8 for a long time but no progress. I am wondering if you could kindly send me the source program and the necessary information about it. I promise they will be used only for research purposes.
Thank you very much for your kind consideration and I am looking forward to your early reply.
Sincerely, yours
Qiqi Zhou

I’m sorry to disturb you in your busy schedule.
I am wondering if you could kindly send me the source code and the necessary information about it. I promise they will be used only for research purposed.

Dr. Benjamin A. Frandsen told me that you had done the excellent AIMD modeling job.

MD

MD potential parameters

以下为该篇论文中的势参数A new transferable interatomic potential for molecular dynamics simulations of borosilicate glasses

Element Partial charge (e)
O −0.945
Si 1.89
B 1.4175
Ca 0.945
Na 0.4725
Ti 1.89
Al 1.4175
Fe3+ 1.4175
Fe2+ 0.945
Mg 0.945
K 0.4725

Bond Aij (eV) ρij (Å) Cij (eV·Å6)
O O 9022.79 0.265 85.0921
Si O 50306.10 0.161 46.2978
B O 206941.81 0.124 35.0018
B B 484.40 0.35 0.0
Si B 337.70 0.29 0.0
Na O 120303.80 0.17 0.0
Ca O 155667.70 0.178 42.2597
Ti O 50126.64 0.178 46.2978
Al O 28538.42 0.172 34.5778
Fe3+ O 8020.27 0.19 0.0
Fe2+ O 13032.93 0.19 0.0
Mg O 32652.64 0.178 27.2810
K O 2284.77 0.29 0.0

第一组

set type 1 charge 1.89 # Si
set type 2 charge 1.4175 # B
set type 3 charge 0.945 # Ca
set type 4 charge -0.945 # O

group Si type 1
group B type 2
group Ca type 3
group O type 4

Bond Aij (eV) ρij (Å) Cij (eV·Å6)
pair_coeff * * 0.00000000 1.000 0.00000000 # others
pair_coeff 1 2 337.70 0.29 0.0
pair_coeff 1 4 50306.10 0.161 46.2978
pair_coeff 2 2 484.40 0.35 0.0
pair_coeff 2 4 206941.81 0.124 35.0018
pair_coeff 3 4 155667.70 0.178 42.2597
pair_coeff 4 4 9022.79 0.265 85.0921

上一次跑的第一组势参数，缺少B-B

pair_coeff * * 0.00000000 1.000 0.00000000 # others
pair_coeff 1 2 337.70000 0.290000 0.0000
pair_coeff 1 4 50306.100 0.161000 46.2978
pair_coeff 2 4 206941.81 0.124000 35.0018
pair_coeff 3 4 155667.70 0.178000 42.2597
pair_coeff 4 4 9022.7900 0.265000 85.0921

第二组

set type 1 charge 1.89 # Si
set type 2 charge 1.4175 # Al
set type 3 charge 1.4175 # B
set type 4 charge 0.945 #Ca
set type 5 charge -0.945 # O

group Si type 1
group Al type 2
group B type 3
group Ca type 4
group O type 5

pair_coeff * * 0.00000000 1.000 0.00000000 # others
pair_coeff 1 3 337.70 0.29 0.0 # Si-B
pair_coeff 1 5 50306.10 0.161 46.2978 # Si-O
pair_coeff 2 5 28538.42 0.172 34.5778 # Al-O
pair_coeff 3 3 484.40 0.35 0.0 # B-B
pair_coeff 3 5 206941.81 0.124 35.0018 # B-O
pair_coeff 4 5 155667.70 0.178 42.2597 # Ca-O
pair_coeff 5 5 9022.79 0.265 85.0921 # O-O

上一次跑的第一组势参数，缺少B-B

pair_coeff * * 0.00000000 1.000 0.00000000 # others
pair_coeff 1 3 337.70000 0.290000 0.0000
pair_coeff 2 2 484.40000 0.350000 0.0000
pair_coeff 1 5 50306.100 0.161000 46.2978
pair_coeff 2 5 28538.420 0.172000 34.5778
pair_coeff 3 5 206941.81 0.124000 35.0018
pair_coeff 4 5 155667.70 0.178000 42.2597
pair_coeff 5 5 9022.7900 0.265000 85.0921

##################################################################################################################################
################################################################################################################################## ##################################################################################################################################
##################################################################################################################################

BMH 势参数, Molecular Dynamics Simulation of the Structure and Properties of CaO-SiO2-CaF2 Slag Systems

CaO-SiO2-CaF2
Atom1 Atom2 Aij(eV) Bij(1/Å) Cij(eV·Å6)
O O 1497693.5 5.88 17.34
O Ca 718136 6.06 8.67
O F 1046135.4 5.88 17.34
O Si 62821.4 6.06 0
Ca Ca 329193.3 6.25 4.34
Ca F 496191.5 6.06 8.67
Ca Si 26686.2 6.25 0
F F 730722.8 5.88 17.34
F Si 43406 6.06 0
Si Si 2163.3 6.25 0

BMH 势参数, Effect of Magnetic Field on CaO–SiO2–CaF2 Mould Flux: New Insight from Molecular Dynamic Simulation

CaO–SiO2–CaF2
Atom 1 Atom 2 Aij (eV) Bij (1/Å) Cij (eV·Å6)
Si Si 2163.3 6.25 0
Si O 62821.4 6.06 0
Si Ca 26686.2 6.25 0
Si F 43406 6.06 0
Ca Ca 329193.3 6.25 4.34
Ca O 718136 6.06 8.67
Ca F 496191.5 6.06 8.67
F F 730722.8 5.88 17.34
F O 1046135.4 5.88 17.34
O O 1497693.5 5.88 17.34

BMH 势参数, Effect of Fluorine on Melt Structure for CaO–SiO2–CaF2 and CaO–Al2O3–CaF2 by Molecular Dynamics Simulations

CaO–SiO2–CaF2
Atom 1 Atom 2 Aij/(eV) ρij/(Å) Cij/(eV·Å6)
Si Si 4142.15 0.16 0
Si Ca 26674.68 0.16 0
Si O 62794.37 0.165 0
Si F 43406.00 0.165 0
Ca Ca 329051.6 0.16 4.355
Ca O 717827.0 0.165 8.67
Ca F 496191.5 0.165 8.67
O O 1497049.0 0.17 17.34
O F 1046135.4 0.17 17.34
F F 730722.8 0.17 17.34
Al Al 4142.149 0.16 0.0
Al Ca 36918.57 0.16 0.0
Al O 86057.58 0.165 0.0
Al F 59481.584 0.165 0.0

BMH 势参数, Effective Mechanism of B2O3 on the Structure and Viscosity of CaO–SiO2–B2O3-based Melts

CaO–SiO2–B2O3
Atom1 Atom2 Aij [eV] Bij [1 Å1] Cij [eV Å6]
O O 1497049.00 5.88 17.34
O B 86057.58 6.06 0
O Si 62794.37 6.06 0
O Ca 717827.00 6.06 8.67
Ca Ca 329051.60 6.25 4.335
Ca Si 26674.68 6.25 0
Ca B 36918.57 6.25 0
Si Si 2162.39 6.25 0
B B 4142.15 6.25 0

CP2K

How to Converge the CUTOFF and REL_CUTOFF

How to Converge the CUTOFF and REL_CUTOFF

Introduction

QUICKSTEP, as with nearly all ab initio Density Functional Theory simulation packages, requires the use of a real-space (RS) integration grid to represent certain functions, such as the electron density and the product Gaussian functions.

QUICKSTEP uses a multi-grid system for mapping the product Gaussians onto the RS grid(s), so that wide and smooth Gaussian functions are mapped onto a coarser grid than narrow and sharp Gaussians.

The electron density is always mapped onto the finest grid.

Choosing a fine enough integration grid for a calculation is crucial in obtaining meaningful and accurate results.

In this tutorial, we will show the reader how to systematically find the correct settings for obtaining a sufficiently fine integration grid for his/her calculation.

This tutorial assumes the reader already has some knowledge of how to perform a simple energy calculation using QUICKSTEP (this can be found in tutorial: Calculating Energy and Forces using Quickstep).

A completed example from an earlier calculation can be obtained from the file converging_grid.tgz that comes with this tutorial.

The calculations were carried out using CP2K version 2.4.

‘’QUICKSTEP’’ Multi-Grid

Before we go through the input file, it is worthwhile to explain how the multi-grid is constructed in QUICKSTEP, and how the Gaussians are mapped onto the different grid levels.

Hopefully this will offer the reader a clear picture of how the key control parameters affect the grids, and thus the overall accuracy of a calculation.

All multi-grid related settings for a calculation is controlled via keywords in MULTIGRID subsection of DFT subsection in FORCE_EVAL.

The number of levels for the multi-grid is defined by NGRIDS, and by default this is set to 4.

The keyword CUTOFF defines the planewave cutoff (default unit is in Ry) for the finest level of the multi-grid.

The higher the planewave cutoff, the finer the grid.

The corresponding planewave cutoffs for the subsequent grid levels (from finer to coarser) are defined by the formula:

Eicut=E1cutα(i−1)

where α has a default value of 3.0, and since CP2K versions 2.0, can be configured by the keyword PROGRESSION_FACTOR.

Therefore, the higher the value of CUTOFF the finer grid for all multi-grid levels.

Having constructed the multi-grid, QUICKSTEP then needs to map the Gaussians onto the grids.

The keyword REL_CUTOFF controls which product Gaussians are mapped onto which level of the multi-grid.

CP2K tries to map each Gaussian onto a grid such that the number of grid points covered by the Gaussian—no matter how wide or narrow—are roughly the same.

REL_CUTOFF defines the planewave cutoff of a reference grid covered by a Gaussian with unit standard deviation (e|→r|2).

A Gaussian is mapped onto the coarsest level of the multi-grid, on which the function will cover number of grid points greater than or equal to the number of grid points e|→r|2 will cover on a reference grid defined by REL_CUTOFF.

Therefore, the two most important keywords effecting the integration grid and the accuracy of a calculation are CUTOFF and REL_CUTOFF.

If CUTOFF is too low, then all grids will be coarse and the calculation may become inaccurate; and if REL_CUTOFF is too low, then even if you have a high CUTOFF, all Gaussians will be mapped onto the coarsest level of the multi-grid, and thus the effective integration grid for the calculation may still be too coarse.

Example: Bulk Si with 8 atoms in a cubic cell

We demonstrate the process using an example based on Bulk Si with 8 atoms in a face centred cubic unit cell.

Template Input File

To systematically find the best CUTOFF and REL_CUTOFF values which are sufficient for a given accuracy (say, 10−6 Ry in total energy), we need to perform a series of single point energy calculations.

It is much easier to use a set of scripts that can automate this process.

To do this, we first write a template input file: template.inp, as shown below:

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&GLOBAL
  PROJECT Si_bulk8
  RUN_TYPE ENERGY
  PRINT_LEVEL MEDIUM
&END GLOBAL
&FORCE_EVAL
  METHOD Quickstep
  &DFT
    BASIS_SET_FILE_NAME  BASIS_SET
    POTENTIAL_FILE_NAME  GTH_POTENTIALS
    &MGRID
      NGRIDS 4
      CUTOFF LT_cutoff
      REL_CUTOFF LT_rel_cutoff
    &END MGRID
    &QS
      EPS_DEFAULT 1.0E-10
    &END QS
    &SCF
      SCF_GUESS ATOMIC
      EPS_SCF 1.0E-6
      MAX_SCF 1
      ADDED_MOS 10
      CHOLESKY INVERSE
      &SMEAR ON
        METHOD FERMI_DIRAC
        ELECTRONIC_TEMPERATURE [K] 300
      &END SMEAR
      &DIAGONALIZATION
        ALGORITHM STANDARD
      &END DIAGONALIZATION
      &MIXING
        METHOD BROYDEN_MIXING
        ALPHA 0.4
        BETA 0.5
        NBROYDEN 8
      &END MIXING
    &END SCF
    &XC
      &XC_FUNCTIONAL PADE
      &END XC_FUNCTIONAL
    &END XC
  &END DFT
  &SUBSYS
    &KIND Si
      ELEMENT   Si
      BASIS_SET SZV-GTH-PADE
      POTENTIAL GTH-PADE-q4
    &END KIND
    &CELL
      SYMMETRY CUBIC
      A     5.430697500    0.000000000    0.000000000
      B     0.000000000    5.430697500    0.000000000
      C     0.000000000    0.000000000    5.430697500
    &END CELL
    &COORD
      Si    0.000000000    0.000000000    0.000000000
      Si    0.000000000    2.715348700    2.715348700
      Si    2.715348700    2.715348700    0.000000000
      Si    2.715348700    0.000000000    2.715348700
      Si    4.073023100    1.357674400    4.073023100
      Si    1.357674400    1.357674400    1.357674400
      Si    1.357674400    4.073023100    4.073023100
      Si    4.073023100    4.073023100    1.357674400
    &END COORD
  &END SUBSYS
  &PRINT
    &TOTAL_NUMBERS  ON
    &END TOTAL_NUMBERS
  &END PRINT
&END FORCE_EVAL
PYTHON

python exercises

Python 字符串
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Python 变量
Python 数据类型
Python 数字
Python 字符串
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Python 变量

创建变量

变量是存放数据值的容器。
与其他编程语言不同，Python 没有声明变量的命令。
首次为其赋值时，才会创建变量。

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x = 5 # x is of type int
x = "Steve" # x is now of type str
print(x)

字符串变量可以使用单引号或双引号进行声明：

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x = "Bill"
# is the same as
x = 'Bill'

变量名称

变量可以使用短名称（如 x 和 y）或更具描述性的名称（age、carname、total_volume）。
Python 变量命名规则：
变量名必须以字母或下划线字符开头
变量名称不能以数字开头
变量名只能包含字母数字字符和下划线(A-z、0-9和_)
变量名称区分大小写（age、Age 和 AGE 是三个不同的变量）
请记住，变量名称区分大小写

向多个变量赋值

Python 允许您在一行中为多个变量赋值：

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x, y, z = "Orange", "Banana", "Cherry"
print(x)
print(y)
print(z)

您可以在一行中为多个变量分配相同的值：

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x = y = z = "Orange"
print(x)
print(y)
print(z)

输出变量

Python 的 print 语句通常用于输出变量。
如需结合文本和变量，Python 使用 + 字符：

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x = "awesome"
print("Python is " + x)

您还可以使用 + 字符将变量与另一个变量相加：

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x = "Python is "
y = "awesome"
z =  x + y
print(z)

对于数字，+ 字符用作数学运算符：

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x = 5
y = 10
print(x + y)

如果您尝试组合字符串和数字，Python 会给出错误：

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x = 10
y = "Bill"
print(x + y)

全局变量

在函数外部创建的变量（如上述所有实例所示）称为全局变量。
全局变量可以被函数内部和外部的每个人使用。
实例
在函数外部创建变量，并在函数内部使用它：

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x = "awesome"

def myfunc():
  print("Python is " + x)

myfunc()

如果在函数内部创建具有相同名称的变量，则该变量将是局部变量，并且只能在函数内部使用。具有相同名称的全局变量将保留原样，并拥有原始值。
实例
在函数内部创建一个与全局变量同名的变量：

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x = "awesome"

def myfunc():
  x = "fantastic"
  print("Python is " + x)

myfunc()

print("Python is " + x)

global 关键字

通常，在函数内部创建变量时，该变量是局部变量，只能在该函数内部使用。
要在函数内部创建全局变量，您可以使用 global 关键字。
实例
如果您用了 global 关键字，则该变量属于全局范围：

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def myfunc():
  global x
  x = "fantastic"

myfunc()

print("Python is " + x)

另外，如果要在函数内部更改全局变量，请使用 global 关键字。
实例
要在函数内部更改全局变量的值，请使用 global 关键字引用该变量：

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x = "awesome"

def myfunc():
  global x
  x = "fantastic"

myfunc()

print("Python is " + x)

Python 数据类型

内置数据类型

在编程中，数据类型是一个重要的概念。
变量可以存储不同类型的数据，并且不同类型可以执行不同的操作。
在这些类别中，Python 默认拥有以下内置数据类型：

类型 type
capitalize() 把首字符转换为大写。
文本类型 str
数值类型 int, float, complex
序列类型 list, tuple, range
映射类型 dict
集合类型 set, frozenset
布尔类型 bool
二进制类型 bytes, bytearray, memoryview

获取数据类型

您可以使用 type() 函数获取任何对象的数据类型：
实例
打印变量 x 的数据类型：

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x = 10
print(type(x))

设置数据类型

在 Python 中，当您为变量赋值时，会设置数据类型：

实例 数据类型
x = “Hello World” str
x = 29 int
x = 29.5 float
x = 1j complex
x = [“apple”, “banana”, “cherry”] list
x = (“apple”, “banana”, “cherry”) tuple
x = range(6) range
x = {“name” : “Bill”, “age” : 63} dict
x = {“apple”, “banana”, “cherry”} set
x = frozenset({“apple”, “banana”, “cherry”}) frozenset
x = True bool
x = b”Hello” bytes
x = bytearray(5) bytearray
x = memoryview(bytes(5)) memoryview

设定特定的数据类型

如果希望指定数据类型，则您可以使用以下构造函数：

实例 数据类型
x = str(“Hello World”) str
x = int(29) int
x = float(29.5) float
x = complex(1j) complex
x = list((“apple”, “banana”, “cherry”)) list
x = tuple((“apple”, “banana”, “cherry”)) tuple
x = range(6) range
x = dict(name=”Bill”, age=36) dict
x = set((“apple”, “banana”, “cherry”)) set
x = frozenset((“apple”, “banana”, “cherry”)) frozenset
x = bool(5) bool
x = bytes(5) bytes
x = bytearray(5) bytearray
x = memoryview(bytes(5)) memoryview

Python 数字

Python 数字

Python 中有三种数字类型：
int
float
complex
为变量赋值时，将创建数值类型的变量：
实例

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x = 10    # int
y = 6.3  # float
z = 2j   # complex

如需验证 Python 中任何对象的类型，请使用 type() 函数：
实例

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print(type(x))
print(type(y))
print(type(z))

Int

Int 或整数是完整的数字，正数或负数，没有小数，长度不限。
实例
整数：

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x = 10
y = 37216654545182186317
z = -465167846

print(type(x))
print(type(y))
print(type(z))

Float

浮动或“浮点数”是包含小数的正数或负数。
实例
浮点：

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x = 3.50
y = 2.0
z = -63.78

print(type(x))
print(type(y))
print(type(z))

浮点数也可以是带有“e”的科学数字，表示 10 的幂。
实例
浮点：

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x = 27e4
y = 15E2
z = -49.8e100

print(type(x))
print(type(y))
print(type(z))

复数

复数用 “j” 作为虚部编写：
实例
复数：

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x = 2+3j
y = 7j
z = -7j

print(type(x))
print(type(y))
print(type(z))

类型转换

您可以使用 int()、float() 和 complex() 方法从一种类型转换为另一种类型：
实例
从一种类型转换为另一种类型：

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x = 10 # int
y = 6.3 # float
z = 1j # complex

# 把整数转换为浮点数

a = float(x)

# 把浮点数转换为整数

b = int(y)

# 把整数转换为复数：

c = complex(x)

print(a)
print(b)
print(c)

print(type(a))
print(type(b))
print(type(c))

注释：您无法将复数转换为其他数字类型

随机数

Python 没有 random() 函数来创建随机数，但 Python 有一个名为 random 的内置模块，可用于生成随机数：
实例
导入 random 模块，并显示 1 到 9 之间的随机数：

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import random

print(random.randrange(1,10))

指定变量类型

有时您可能需要为变量指定类型。这可以通过 casting 来完成。 Python 是一门面向对象的语言，因此它使用类来定义数据类型，包括其原始类型。
因此，使用构造函数完成在 python 中的转换：
int() - 用整数字面量、浮点字面量构造整数（通过对数进行下舍入），或者用表示完整数字的字符串字面量
float() - 用整数字面量、浮点字面量，或字符串字面量构造浮点数（提供表示浮点数或整数的字符串）
str() - 用各种数据类型构造字符串，包括字符串，整数字面量和浮点字面量
实例
整数：

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x = int(1)   # x 将是 1
y = int(2.5) # y 将是 2
z = int("3") # z 将是 3

浮点数：

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x = float(1)     # x 将是 1.0
y = float(2.5)   # y 将是 2.5
z = float("3")   # z 将是 3.0
w = float("4.6") # w 将是 4.6

字符串：

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x = str("S2") # x 将是 'S2'
y = str(3)    # y 将是 '3'
z = str(4.0)  # z 将是 '4.0'

Python 字符串

字符串字面量

python 中的字符串字面量由单引号或双引号括起。

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print("Hello")
print('Hello')

用字符串向变量赋值

通过使用变量名称后跟等号和字符串，可以把字符串赋值给变量：

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a = "Hello"
print(a)

多行字符串

您可以使用三个引号将多行字符串赋值给变量：

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a = """Python is a widely used general-purpose, high level programming language. 
It was initially designed by Guido van Rossum in 1991 
and developed by Python Software Foundation. 
It was mainly developed for emphasis on code readability, 
and its syntax allows programmers to express concepts in fewer lines of code."""
print(a)

或三个单引号：

字符串是数组

像许多其他流行的编程语言一样，Python 中的字符串是表示 unicode 字符的字节数组。
但是，Python 没有字符数据类型，单个字符就是长度为 1 的字符串。
方括号可用于访问字符串的元素。
实例
获取位置 1 处的字符（请记住第一个字符的位置为 0）：

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a = "Hello, World!"
print(a[1])

裁切

您可以使用裁切语法返回一定范围的字符。
指定开始索引和结束索引，以冒号分隔，以返回字符串的一部分。
实例
获取从位置 2 到位置 5（不包括）的字符：

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b = "Hello, World!"
print(b[2:5])

负的索引

使用负索引从字符串末尾开始切片：
实例
获取从位置 5 到位置 1 的字符，从字符串末尾开始计数：

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b = "Hello, World!"
print(b[-5:-2])

字符串长度

如需获取字符串的长度，请使用 len() 函数。
实例
len() 函数返回字符串的长度：

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a = "Hello, World!"
print(len(a))

字符串方法

Python 有一组可用于字符串的内置方法。
实例
strip() 方法删除开头和结尾的空白字符：

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a = " Hello, World! "
print(a.strip()) # returns "Hello, World!"

实例

lower() 返回小写的字符串：

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a = "Hello, World!"
print(a.lower())

实例

upper() 方法返回大写的字符串：

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a = "Hello, World!"
print(a.upper())

实例

replace() 用另一段字符串来替换字符串：

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a = "Hello, World!"
print(a.replace("World", "Kitty"))

实例

split() 方法在找到分隔符的实例时将字符串拆分为子字符串：

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a = "Hello, World!"
print(a.split(",")) # returns ['Hello', ' World!']

检查字符串

如需检查字符串中是否存在特定短语或字符，我们可以使用 in 或 not in 关键字。
实例
检查以下文本中是否存在短语 “ina”：

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txt = "China is a great country"
x = "ina" in txt
print(x)

字符串级联（串联）

如需串联或组合两个字符串，您可以使用 + 运算符。
实例
将变量 a 与变量 b 合并到变量 c 中：

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a = "Hello"
b = "World"
c = a + b
print(c)

实例
在它们之间添加一个空格：

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a = "Hello"
b = "World"
c = a + " " + b
print(c)

字符串格式

正如在 Python 变量一章中所学到的，我们不能像这样组合字符串和数字：
实例

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age = 63
txt = "My name is Bill, I am " + age
print(txt)

但是我们可以使用 format() 方法组合字符串和数字！
format() 方法接受传递的参数，格式化它们，并将它们放在占位符 {} 所在的字符串中：
实例
使用 format() 方法将数字插入字符串：

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age = 63
txt = "My name is Bill, and I am {}"
print(txt.format(age))

format() 方法接受不限数量的参数，并放在各自的占位符中：

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quantity = 3
itemno = 567
price = 49.95
myorder = "I want {} pieces of item {} for {} dollars."
print(myorder.format(quantity, itemno, price))

您可以使用索引号 {0} 来确保参数被放在正确的占位符中：

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quantity = 3
itemno = 567
price = 49.95
myorder = "I want to pay {2} dollars for {0} pieces of item {1}."
print(myorder.format(quantity, itemno, price))

字符串方法

方法 描述
capitalize() 把首字符转换为大写。
casefold() 把字符串转换为小写。
center() 返回居中的字符串。
count() 返回指定值在字符串中出现的次数。
encode() 返回字符串的编码版本。
endswith() 如果字符串以指定值结尾，则返回 true。
expandtabs() 设置字符串的 tab 尺寸。
find() 在字符串中搜索指定的值并返回它被找到的位置。
format() 格式化字符串中的指定值。
format_map() 格式化字符串中的指定值。
index() 在字符串中搜索指定的值并返回它被找到的位置。
isalnum() 如果字符串中的所有字符都是字母数字，则返回 True。
isalpha() 如果字符串中的所有字符都在字母表中，则返回 True。
isdecimal() 如果字符串中的所有字符都是小数，则返回 True。
isdigit() 如果字符串中的所有字符都是数字，则返回 True。
isidentifier() 如果字符串是标识符，则返回 True。
islower() 如果字符串中的所有字符都是小写，则返回 True。
isnumeric() 如果字符串中的所有字符都是数，则返回 True。
isprintable() 如果字符串中的所有字符都是可打印的，则返回 True。
isspace() 如果字符串中的所有字符都是空白字符，则返回 True。
istitle() 如果字符串遵循标题规则，则返回 True。
isupper() 如果字符串中的所有字符都是大写，则返回 True。
join() 把可迭代对象的元素连接到字符串的末尾。
ljust() 返回字符串的左对齐版本。
lower() 把字符串转换为小写。
lstrip() 返回字符串的左修剪版本。
maketrans() 返回在转换中使用的转换表。
partition() 返回元组，其中的字符串被分为三部分。
replace() 返回字符串，其中指定的值被替换为指定的值。
rfind() 在字符串中搜索指定的值，并返回它被找到的最后位置。
rindex() 在字符串中搜索指定的值，并返回它被找到的最后位置。
rjust() 返回字符串的右对齐版本。
rpartition() 返回元组，其中字符串分为三部分。
rsplit() 在指定的分隔符处拆分字符串，并返回列表。
rstrip() 返回字符串的右边修剪版本。
split() 在指定的分隔符处拆分字符串，并返回列表。
splitlines() 在换行符处拆分字符串并返回列表。
startswith() 如果以指定值开头的字符串，则返回 true。
strip() 返回字符串的剪裁版本。
swapcase() 切换大小写，小写成为大写，反之亦然。
title() 把每个单词的首字符转换为大写。
translate() 返回被转换的字符串。
upper() 把字符串转换为大写。
zfill() 在字符串的开头填充指定数量的 0 值。 

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Gaussian

思想家公社的门口

Multiwfn相关

使用Multiwfn做拓扑分析以及计算孤对电子角度
使用Multiwfn和VMD绘制平均局部离子化能(ALIE)着色的分子表面图（含视频演示）
通过独立梯度模型(IGM)考察分子间弱相互作用
使用Multiwfn做IGMH分析非常清晰直观地展现化学体系中的相互作用
Multiwfn支持的弱相互作用的分析方法概览
Multiwfn可以计算的分子描述符一览

关键词：cp2k

关键词：泛函

关键词：基组

关键词：弱相互作用、B3LYP泛函、DFT-D3色散矫正

关键词：基组重叠误差、BSSE

关键词：Multiwfn

杂谈

关于Gaussian的一些教程

Paper

Paper sentences accumulation

2021/06/07

  • Molecular dynamics (MD) simulations were carried out to determine the influence of alkalis (K2O and Na2O; up to 10%) on the local structural order, bonding networks and fluidity of molten Al2O3-CaO-SiO2 system (2223 K).

  • Experimental results on the system indicate an increasing viscosity in the presence of K2O and a decreasing trend for Na2O.

  • Attributing these differences to local distortions and the sizes of K+ and Na+ ions, theoretical investigations on these systems have predicted a reduction in viscosity for both alkalis.

  • The literature is rich in potentials configured for the simulation of silicate systems, for example the Vessal potential [14], [15], [16], [17], [18], the Matsui potential [19], [20], [21], or the Guillot–Sator (GS) potential [22]. None of these potentials take boron into account.

  • New parameter values are proposed for the empirical potentials used to describe SiO2–B2O3–Na2O alkali borosilicate glass systems. They are based on Buckingham potentials, but include dependence between the fitting parameters and the glass chemical composition to improve the representation of the complex environment around the boron atoms.

  • This tutorial is designed to illustrate how to relax the structure of a system (without changing the cell dimensions) using CP2K. We use the relaxation of a water (H2O) molecule as an example.

Stabilization mechanism of arsenic-sulfide slag by density functional theory calculation of arsenic-sulfide clusters

Abstract
Introduction
Computational details
Results and discussion
Conclusion

Highlights

  • A systematic DFT study was carried out to analyze the various … cluster.
  • The … structure possesses the highest stability.
  • The relationship between … structure and stability of … slag was confirmed.
  • The consumption of 4p-orbital in … by … atoms could further improve the stability of … slag.
  • The stabilized experiment was a good agreement with DFT results.
  1. Abstract
  • Stabilization of … slag is of high importance to …
  • However, the molecular understanding on the stability of … is missing, which in turn restricts the development of robust approach to solve the challenge.
  • In this work, we investigated the structure-stability relationship of … with adopting various … clusters as prototypes by density functional theory (DFT).
  • Results showed that the configuration of … is the most stable structure amongst the candidates by the analysis of energies and bonding characteristics.
  • The high stability is explained by orbital composition that the 4p-orbital (As) binding with 3p-orbital (S) decreases energy level of highest occupied molecular orbital (HOMO).
  • Inspired from the calculations, an … method was successfully proposed and achieved to promote the stabilization of …
  • Typically, the … concentration from the leaching test of … is only … mg/L, which is much lower than the value from …
  1. Introduction
  • … is a typical deadly pollutant in the … of … industries, which is normally detoxified by chemical precipitation.
  • As such, more than … tons of … could be produced annually.
  • However, the relatively weak stability of … slag in ambient conditions would allow … release again into …, causing severely …
  • To stabilize …, solidification methods have been developed, but they suffer from relatively high enlargement ratio(放大倍数) and cost, and complicated process.
  • Most recently, hydrothermal treatment（水热处理） was proposed to dispose … by creating necessary conditions to re-build the structure of …
  • As a consequence, the structure of … was modified with improved stability towards the harsh environment.
  • However, the understanding of relationship between … structure and stability of … is missing, which in turn hampers a further development of … stabilization.
  • In recent years, the rapid development of clusters science boosts the fundamental research of materials since clusters structure can be regarded as a basis to understand physicochemical properties.
  • Currently, limited examples are emerging on the cluster structure of … compounds.
  • For instance, based on Gaussian-03 scheme calculation, Yang et al. investigated the structure of … clusters and predicted that the clusters are stable.
  • Guillermo et al. revealed the geometrical structure of … clusters with combination of results and analysis of mass spectroscopy.
  • Although great progress has been made, these researches are irrelevant with stabilization of …
  • As revealed, the … structure is an important factor to the stability of … and the clusters can be regarded as basic molecular skeleton in …
  • Therefore, the structure of … clusters and further the correlation of cluster structure with its properties, typically the bonding behavior and electronic information, are very important for a rational design of stabilization strategy.
  • However, seldom effort has been paid to figuring out how the S-to-As molar ratio influences the clusters structure and why specific structure could increase the slag stability.
  • Here we investigated the geometric structure of various As‒S clusters based on density functional theory to simulate their structures.
  • Typically, the cluster energy, and electron and orbital characteristics were analyzed, and the As‒S interaction behavior was revealed.
  • The bonding characteristics and energy analysis demonstrate the structure of … is of the highest stability.
  • Furthermore, the orbital composition indicates that the stability of this structure is stemmed from the 4p-orbital in As atom binding with 3p-orbital of S atom, which brings down the HOMO energy level.
  • Motivated by this theoretical result, rational design was conducted on hydrothermal treatment of As‒S slag by adding pure S powders.
  • Such a hydrothermally treated slag possesses a very high stability as revealed by leaching experiment that the As concentration in the leaching solution is only 0.8 mg/L.
  1. Computational details
  • The initial configuration searches for the … clusters were based on two steps.
  • Firstly, By the ABCluster 2.0 global search technique combined with the GFN1-xTB, more than 5000 isomers(异构体) structure for (As2S2)n, (As2S3)n and (As2S5)n (n = 1–8) clusters were predicted.
  • Secondly, the first three minimum structure were optimized using B3LYP functional with the all-electron small basis sets (def2SVP) by Gaussian 09 package.
  • Vibrational frequency calculations were carried out at the same level to check the local minima structure on the potential energy surface else.
  • Meanwhile, spin multiplicity is fully considered during the geometric optimization process.
  • After completion of initial configuration searches work, we selected B3LYP functional with the all-electron def2TZVP basis sets to refine the single-point energy calculation.
  • Implicit solvent model is assessed to correct potential energy surface and all-electron properties caused by solvent effect in all process.
  • Thirdly, the wave function analysis including the structural parameter was carried out by Multiwfn 3.5 to realize orbital information and electronic properties, and the Raman spectrum was calculated.
  • To verify the accuracy of the optimization method, we employed different functionals and basic sets (e.g., the theoretical binding energy, bond length and vibration frequency of As2 dimer(二聚物) and S2 dimer), and the results were compared to the measured experiments data, as shown in Table S1.
  • Electron Localization Function (ELF) was calculated while we choose the isosurface value is 0.7 for As16S16 cluster, As16S24 cluster and As16S40 cluster.
  1. Results and discussion
  • In this research, (As2S2)n, (As2S3)n and (As2S5)n (n = 1–8) were adopted in the calculation.
  • The neutral (As2S2)n and (As2S3)n (n = 1–8) clusters were typical As‒S compounds according to the phase diagram.
  • The metastable compound As2S5 was also taken to investigate the effect of S-to-As molar ratio of on the cluster properties.
  • The local minima structure of As‒S clusters was regarded as the stable state to exhibit chemical properties.
  • The effect of S-to-As molar ratio on the clusters structure was analyzed by the optimized geometrical structures of (As2S2)n, (As2S3)n, and (As2S5)n (n = 1–8) by taking n = 8 as the representatives (Fig. 1).
  • The structure evolution for these clusters with different size was systematically illustrated in Figs. S2–S4.
  • Typically, the symmetry of (As2S2)n, (As2S3)n, and (As2S5)n (n = 2–8) belonged to C1, all of which were of irregular structures (Figs. 1a–c and S2–S4), this structural evolution is similar to the previous work (Yang et al., 2013, Hou et al., 2014).
  • On the other hand, the voluminosity of the clusters tends to decrease with the increase of molar ratio of S (Fig. 1d) (Lu and Chen, 2012).
  • This may be caused by the relatively small atomic radii of S.
  • Moreover, the S multimers were found distributing on the exterior of (As2S5)n cluster (n = 1–8), which looks like S multimers covering (As2S3)n cluster (n = 1–8).
  • As mentioned, the As2S5 is a metastable structure (Hansen et al., 1958), and thus, it is easy to understand the formation of S multimers-covering-(As2S3)n structure.
  • Further evidences on forming this structure will be presented below.
  • Here for the convenience of depiction, As2S5 and (As2S5)n (n = 1–8) were still used in the following content.
  1. Bonding characteristics of the clusters

  • To understand the differences of structure, As‒S bonding characteristics are detailly analyzed.

  • The average distance of As and S to the cluster center (in brief: As-c distance/S-c distance) was measured (Lu and Chen, 2012b).

  • As seen in Fig. 2a, there is no obvious difference between As-c distance and S-c distance for (As2S2)n and (As2S3)n (n = 1–8).

  • However, S-c distance turns into much larger than As-c distance for (As2S5)n when n > 3.

  • Typically, when n = 8, the distance difference of S-c to As-c reaches as high as ~3 Å.

  • That is to say, in the case of (As2S5)n, the S multimers get rid of cluster confinement and stably occupies the exterior shell of the cluster, which is in line with the observation of the structure in Fig.1.

  • Electronic state of the atoms in the clusters was investigated by Hirshfeld charge (Lu and Chen, 2012b).

  • As shown in Fig. 2b, the Hirshfeld charge of As increases slightly, which reveals that the metallic nature of As gradually increases.

  • As seen in Fig. 2c, more and more S atoms present positive charge with the increase of S-to-As ratio.

  • It suggests that S atoms have a tendency to aggregate around As atoms and form S multimer.

  • This could possibly be explained by the S multimers distributing on the outer shell of the (As2S5)n and the (As2S3)n acting as the core of (As2S5)n.

  • Noticeably, it has been verified that the (As2S5)n is metastable compound.

  • Therefore, based on the analysis of calculation data, the (As2S5)n is most possibly composed of (As2S3)n clusters covered with S dimers on the exterior shell.

  • This is due to the excess S present in the chemical formula of As2S5.

  • Further bonding information on (As2S5)n clusters were studied by measuring their average length of As‒As, As‒S and S‒S bonds as well as the Mayer bond order (Lu and Chen, 2018).

  • In Fig. 2c, the results show the bond length is irrelevant to the cluster size n, and the order of bond length follows LAs‒As (>2.5 Å) > LAs‒S (2.18 Å) > LS‒S (2.07 Å).

  • Specifically, the Mayer bond order of As‒As is nearly 0, which proves that the As‒As bond has totally broken up.

  • In addition, the Mayer bond order of As‒S and S‒S are nearly 1, which suggests that the strength of S‒S bond is close to the As‒S bond.

  • This indirectly proves the co-presence of S multimers and As‒S clusters in (As2S5)n, which is corresponding to the analysis of Hirshfeld charge and As/S-c distance.

  • Building on the discussion of the bonding features of various clusters, the increase of S molar ratio would induce the formation of S multimers exteriorly interacting with the As‒S clusters.

  • This sufficiently verifies the above opinion on which the stable structure of (As2S5)n (n = 1–8) is S multimers-covering-(As2S3)n (n = 1–8) structure.

  • The effect of this configuration on the stability of As compound has also been investigated.

  1. Energy analysis of the clusters
  • Here we defined various energies to measure the cluster stability.
  • The formation energy (Ef), the average binding energies per atom (Eb), the fragmentation energies (∆E), and the second order energy differences (∆2E) (Fig. S5) are used to study the stability of various clusters (Li et al., 2018, Li et al., 2017, Song et al., 2017). The definition formula of these energies was present in Text S1.

The formation energy was shown in Fig. 3a that the energy value has no intensive variation with the increase of n for different clusters. The formation energy per monomer is the 26.0 eV (As2S5), 18.0 eV (As2S3), and 14.5 eV (As2S2). This implies that the formation energy of As2S5 is among the largest in these clusters and thus, it is the most stable compound among them. In fact, according to the above analysis of bonding characteristics, (As2S5)n (n = 1–8) most possibly consists of S multimers connecting with the shell of (As2S3)n (n = 1–8) clusters. That means the stability of purely (As2S3)n (n = 1–8) could be marvelously enhanced by the interaction of excess S atoms, which is interesting for the stabilization of As‒S compounds. In addition, the binding energy was calculated. As seen in Fig. 3b, the binding energy of As‒S bond for these three kinds of clusters has no obvious difference. The high-to-low sequence is 3.74 eV [(As2S5)n] > 3.70 eV [(As2S3)n] > 3.66 eV [(As2S2)n]. Moreover, fragmentation energy of various clusters was measured. As shown in Fig. 3c, the (As2S5)n (n = 1–8) cluster is of the highest fragmentation energy (>27 eV). Thus, it is too stable to dissociate into monomer As2S5. This is in good agreement with the results of formation energy. This sufficiently demonstrates the high stability of (As2S5)n (n = 1–8), which should be resulted from their unique structure S multimers-covering-(As2S3)n.

  1. Orbital composition of the clusters
    To explain the formation energy differences, electronic information and orbital composition were employed to seize the degree of stability in electron level. ELF and corresponding S‒As‒S plane projection maps were acquired based on Atoms-In-Molecules theory (Fig. 4). (As2S2)8, (As2S3)8, and (As2S5)8 were applied as typical examples (Lu and Chen, 2011a). Noticeably, isosurface with larger area represents a higher degree of electron localization, which means electron in this area would be difficult to exchange. From Fig. 4a–c, the lone pair electrons in green color has an obviously high area and hence, the localization degree for such type of electrons is high. The S‒As‒S plane projection maps in Fig. 4d–f reveal that the lone pair electrons (green) are originated from S. Moreover, with the increase of S molar ratio, the green area increases accordingly, which mainly distributes on the exterior section of clusters. As a result, the (As2S5)8 could be regarded as an As‒S cluster [smaller than (As2S5)8] being protected by a very stable S shell. More importantly, this could explain very well the results of energy analysis above that the (As2S5)8 possesses the highest stability among the three types of clusters.

Partial density of states (PDOS) map and corresponding composition analysis of atomic orbitals were taken to further understand the mechanism of high stability of (As2S5)n clusters (Lu and Chen, 2011b). The As16S16, As16S24, and As16S40 [(As2Sx)n, n = 8)] were adopted as typical examples. As seen in Fig. 5a, the Fermi level decreases when promoting the S molar ratio. Interestingly, the HOMO correspondingly decreases with the Fermi level while the LUMO stays still. In addition, the p orbital of S contributes most in the molecular orbitals in these three clusters. Fig. 5b presents atomic orbitals composition. Based on Fig. 5b, the energy gap (the differences of HOMO and LUMO/EH-Lgap) was calculated that As16S40 (2.269 eV) > As16S24 (2.165 eV) ≫ As16S16 (1.559 eV).

Basically, the configuration of valence electrons for As and S atoms are 4s24p3 (3 half-full p orbitals) and 3s24p4 (1 full p orbital and 2 half-full p orbitals), respectively. When the atomic ratio of S to As reached 3:2, all the half-full p orbitals of As and S atoms participated in the formation of As‒S bonding. In this case, the HOMO was composed of the remaining full 3p orbital of S atom. When the atomic ratio is less than 3:2, there remains half-full 4p orbitals for As atom, which forms HOMO with the full p orbital of S atom. No wondering, this process promotes the energy level of HOMO, which is not conducive to stabilize the As‒S compounds. In turn, when the atomic ratio is larger than 3:2, the HOMO dominantly consisted of half-full 3p orbitals, which has a relatively low energy level.

That is to say, the stability of As‒S compound in this high atomic ratio increases. Briefly speaking, the analysis of orbital composition can elucidate very well the above results on the high stability of the As‒S compounds with S multimers-covering-(As2S3)n structure [chemical formula: (As2S5)n (n = 1–8)].

  1. A batch of stabilized treatment

The high chemical stability of the S multimers-covering-(As2S3)n structure inspires the development of stabilization of As‒S slag. In general, the As‒S slag mainly consists of As2S3 structure, which is not stable when exposing to outer environment. Incorporation of excess S to interact with the As‒S slag would possibly produce S multimers-covering-(As2S3)n structure to increase the stability.

In this context, we designed experiments to verify the prospect of As‒S compound stabilization by using the reaction between S and the raw As2S3 powders as a prototype. In brief, powder mixture of S and As2S3 was treated in hydrothermal conditions (Scheme 1 and Text S2). Control experiment was also carried out by hydrothermal treatment of the powders mixture of S and As2S3, and stabilized As‒S compounds was obtained, named for SA. The pictures of the raw materials and the product were given in Scheme 1 that the final product became shinning bulk from the coarse powders. Moreover, Toxicity Characteristic Leaching Procedure (TCLP) based on USEPA Method 1311 was used to examine the stability of these compounds (USEPA, 1992). The leaching concentration of As from the As2S3 raw materials reached as high as 139.4 mg/L while the value from the hydrothermally treated As2S3 decreased to 54.5 mg/L. However, the product obtained by the reaction of S with As2S3 exhibited a low leaching capability of 0.8 mg/L. According to the identification standards for hazardous wastes of China (GB5080.3-2007), the upper limit for the As leaching concentration is 5 mg/L. Apparently, the As‒S compound has been stabilized by the chemical interaction with the excess S, which is in line with the results of DFT calculation analysis. The materials were characterized by the XRD and Raman techniques (Fig. 6). The XRD results illustrate non-crystal S have transformed into sample SA from S raw materials in Fig. 6a. The Raman spectrum is fitted to overlapping peaks between 300 and 400 cm−1, which are mainly contributed to As‒S bond in As2S3 state (Kovalskiy et al., 2017, Mochalov et al., 2018) from Fig. 6b–d. Based on energy-dispersive spectroscopy (EDS) (Fig. S6), the atomic ratio of As and S is 16.8% and 83.2%, respectively. It is noticed that the sulfur is mainly consisting of S2 and S8 ring (Fig. 6b), which is a good agreement in the previous work (Kovalskiy et al., 2017, Mochalov et al., 2018). From these characterizations, the S multimers-covering-As2S3 structure were found in the final product. Consider that the calculated Raman activity of As16S40 cluster (Fig. S7), this is corresponding to the discovery from the results that the S multimers-covering-As2S3 is of the highest stability.

  • In summary, DFT calculations were applied to investigate the structural stability of various As‒S clusters.
  • The analysis of structural character suggest the S multimers-covering-As2S3 configuration possessed the highest stability amongst the candidates.
  • The binding energy and formation energy of S multimers-covering-As2S3 is 3.74 and 26.0 eV, respectively.
  • In typical, the electronic information confirms the stabilized mechanism is contributed to the 4p-orbital (As) binding with 3p-orbital (S) decreases energy level of HOMO.
  • Motivated by the calculation results, a rational design was proposed by adding additional S into the As2S3 powders in the hydrothermal reaction to produce a chemically stable compound.
  • Based on the standard toxicity leaching experiments, the As concentration in the leachate is only 0.8 mg/L, which is far lower than the As‒S compounds without interaction with S.
  • The theoretical understanding on the structure-stability relationship of As‒S clusters and the inspired treatment method open a hopeful window for large-scale stabilization treatment of As‒S slag.
VASP

vasp_Si

Liquid Si - Standard MD

POSCAR

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Si cubic diamond conventional cell
  5.43100000000000
 1.00000000 0.00000000 0.00000000
 0.00000000 1.00000000 0.00000000
 0.00000000 0.00000000 1.00000000
  Si
  8
Direct
  0.00000000 0.00000000 0.00000000
  0.75000000 0.25000000 0.75000000
  0.00000000 0.50000000 0.50000000
  0.75000000 0.75000000 0.25000000
  0.50000000 0.00000000 0.50000000
  0.25000000 0.25000000 0.25000000
  0.50000000 0.50000000 0.00000000
  0.25000000 0.75000000 0.75000000

KPOINTS

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Gamma
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INCAR

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ISMEAR = 0
IBRION = 0
MDALGO = 2
ISIF = 2
SMASS = 1.0
SIGMA = 0.1
LREAL = Auto
ALGO = VeryFast
PREC = Low
ISYM = 0
TEBEG = 2000
NSW = 50
POTIM = 3.0
NCORE = 2
  • To select a molecular dynamics calculation set IBRION=0.
  • By selecting MDALGO=2 and ISIF=2 we select the NVT ensemble using the Nose-Hoover thermostat.
  • The tag SMASS specifies the Nose mass, which is a ficitional mass for the fictional coordinate of the heat bath. The choice of SMASS=1.0 should work well for this tutorial.
  • Since we are dealing with a super cell, we set LREAL=Auto. In this mode the projection operators are evaluated in real space. This should speed up the calculation while being slightly less accurate then the evaluation of the operators in reciprocal space.
  • To significantly speed up the calculations we use ALGO=VeryFast and PREC=Low. This is ok for this tutorial example but for more precise results these flags should be used with caution!
  • A time step of 3 femtoseconds (POTIM=3.0) is employed in this example, which should be ok for many applications of Si.
  • The tag NCORE=2 specifies that the parallelization is done such that 2 cores share the work on one orbital. This means that for e.g. 8 cores 4 different orbitals would be treated simultaneously, where for each orbital two plane-wave coefficients would be calculated simultaneously.

POTCAR

CP2K

cp2k关键词思维导图

关键词列表查询

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&GLOBAL
  PROJECT CP2K
  RUN_TYPE  MD/GEO_OPT/CELL_OPT/ENERGY_FORCE/ENERGY/BAND(过渡态)/VIBRATIONAL_ANALYSIS
  PRINT_LEVEL

&MOTION
  &CELL_OPT  
    EXTERNAL_PRESSURE
    KEEP_ANGLES
    KEEP_SYMMETRY
    MAX_ITER
    MAX_DR
    MAX_FORCE
    RMS_DR
    RMS_FORCE
    TYPE
    OPTIMIZER
    CG
  &GEO_OPT #几何结构优化
    MAX_ITER
    OPTIMIZER
    TYPE
  &GEO_OPT #DIMER过渡态
    MAX_ITER
    OPTIMIZER
    TYPE
    TRANSITION_STATE
      METHOD
      DIMER
        DR
        ANGLE_TOLERANCE
        INTERPOLATE_GRADIENT
        DIMER_VECTOR
        ROT_OPT
          OPTIMIZER
          MAX_ITER
          MAX_DR
          MAX_FORCE
          CG
  &BAND #NEB过渡态
    NPROC_REP
    NUMBER_OF_REPLICA
    BAND_TYPE
    ALIGN_FRAMES
    ROTATE_FRAMES
    K_SPRING
  &MD #分子动力学模拟
    ENSEMBLE #系综
    STEPS #总步数
    TIMESTEP #时间步长
    TEMPERATURE #温度
    ANNEALING #退火温度系数
    &THERMOSTAT #
      TYPE #热浴类型
      REGION
      &NOSE #热浴参数
        LENGTH
        YOSHIDA
        MTS
        TIMECON
  &PRINT #轨迹/速度/重启文件的保存频率
    TRAJECTORY
    VELOCITIES
    RESTART_HISTORY
  &CONSTRAINT #固定原子
  &FREE ENERGY #自由能势面

&VIBRATIONAL_ANALYSIS #频率计算
  DX #有限位移距离
  INTENSITIES #计算IR强度
  NPROC_REP #每个结构使用核数
  FULLY_PERIODIC #是否周期性

&FORCE_EVAL
  METHOD #QS计算方法
  STRESS_TENSOR #晶胞变化打开
  &SUBSYS
    &CELL #晶胞参数
    &COOED #坐标
    &TOPOLOGY #坐标文件
    &KIND #
      ELEMENT #元素
      BASIS_SET #基组
      POTENTIAL #赝势
      &DFT_PLUS_U #DFT+U参数
      &BS #设置磁性初猜
      &MAGNETIZATION #设置磁性初猜
      
  &DFT
    BASIS_SET_FILE_NAME #基组文件
    POTENTIAL_FILE_NAME #赝势文件
    LSD/UKS #开壳层计算
    WFN_RESTART_FILE_NAME #读取波函数
    MULTIPLICITY #自旋多重度
    RELAX_MULTIPLICITY #自动优化自旋多重度
    CHARGE #整体电荷
    PLUS_U_METHOD #开启DFT+U
    SURFACE_DIPOLE_CORRECTION
    SURF_DIP_DIR
    EXCITATIONS #激发态
    &KPOINTS
      SCHEME #自动K点设定
      SYMMETRY
      VERBOSE
      FULL_GRID
    &QS
      EPS_DEFAULT #控制所有EPS整体精度
      EXTRAPOLATION #波函数外推
      EXTRAPOLATION_ORDER #波函数外推
    &MGRID #网格套数与截断能
      NGRIDS
      CUTOFF
      REL_CUTOFF
    &POISSON
    &SCF #对角化方法
      SCF_GUESS #读取波函数
      EPS_SCF #收敛精度
      MAX_SCF #最大步数
      ADDED_MOS #额外计算的分子轨道数量
      CHOLESKY #密度矩阵求逆方法
      &SMEAR #电子smearing
        METHOD
        ELECTRONIC_TEMPERATURE
      &DIAGONALIZATION #对角化算法
        ALGORITHM
        EPS_ADAPT
      &MIXING #电子密度混合
        METHOD
        ALPHA
        BETA
        NBROYDEN
      &PRINT #输出波函数
    &SCF(OT方法)
      SCF_GUESS #读取波函数，或ATOMIC初猜
      EPS_SCF #收敛精度
      MAX_SCF #最大步数
      &OT #OT算法
        MINIMIZER #
        LINESEARCH #
        PRECONDITIONER #
        ENERGY_GAP
    &OUTER_SCF #若不收敛，进行圈外SCF
      EPS_SCF
      MAX_SCF
    &XC
      &XC_FUNCTIONAL
        PBE #泛函
        &LIBXC #自定义泛函
      &vdW_POTENTIAL #范德华校正
        DISPERSION_FUNCTIONAL
        &PAIR_POTENTIAL
        &NON_LOCAL
    &PRINT
      &DOS #态密度
      &PDOS #态密度
        NLUMO
        COMPONENTS
        &LDOS
          COMPONENTS
          LIST
      &LOWDIN #原子电荷
      &HIRSHFELD #原子电荷
      &MULLIKEN #原子电荷
      &E_DENSITY_CUBE #电荷/自旋密度
        FILENAME
        STRIDE
      &ELF_CUBE #
        FILENAME
        STRIDE
      &MO_CUBES #分子轨道
        FILENAME
        STRIDE
      &V_HARTREE_CUBE #静电势
        FILENAME
        STRIDE
      &MOMENTS #偶极矩
  &PRINT FORCES #输出原子受力