1. 知网导出

1. 杨健. 铝合金熔体杂质元素相互作用及偏析机理的研究[D].上海交通大学,2020.DOI:10.27307/d.cnki.gsjtu.2018.000263.
2. 马建波. 镍基合金熔体局域结构的从头算分子动力学研究[D].上海交通大学,2017.

量子化学教程

参考资料 量子化学教程 黄明宝

量子力学基础部分

1 薛定谔方程

• 哈密顿算子

$$
H=-\frac{\hbar^{2}}{2 m} \frac{\partial^{2}}{\partial x^{2}}+V(x, t)
$$

• 薛定谔方程

$$
-\frac{\hbar^{2}}{2 m} \frac{\mathrm{d}^{2}}{\mathrm{~d} x^{2}} \psi(x)+V(x) \psi(x)=E \psi(x)
$$

薛定谔方程

箱中粒子

$$
\psi_{n}(x)=\left{\begin{array}{ll}
\left(\frac{2}{l}\right)^{1 / 2} \sin \frac{n \pi}{l} x, & 0<x<l \
0, & x \leqslant 0, x \geqslant l
\end{array}\right.
$$

一维谐振子

2 算子

括号标记法

定义

括号运算规则

算子

算子

算子是一种规则，它把给出的某个函数变为另外的对应函数

平方根算子

微分算子

Laplace算子

函数算子

线性算子

微分算子、Laplace算子是，平方根算子不是

算子的加法

算子的乘法

计算法则

线性算子乘积

服从结合律，不服从交换律

对易子

• [A,B]=AB-BA, 对易子是一个算子

算子的对易

• [A,B]=0， 0是零算子，算子对易才满足交换律

算子恒等式

• （A+B）C=AC+BC

• A（B+C）=AB+AC

算子的本征函数和本征值

定义：Af(x)=kf(x)，k为本征值，f(x)为本征函数，f(x)不为0，可以为0

本征函数集

简并现象和简并度

多个线性独立的本征函数对应于同一个本征值

本征函数组合

线性算子A对应于同一本征值的本征函数的线性组合仍是其本征函数

线性算子A对应于不同同一本征值的本征函数的线性组合不是其本征函数

算子和量子力学

量子力学中的算子

量子力学算子

• 假设：对于每一个物理量，有一个对应的量子力学算子

• 构造方法

动量分量算子

动能算子

哈密顿（能量）算子

平均值

厄米算子

3 角动量

4 氢原子

5 量子力学的定理

按本征函数的展开

可对易算子的本征函数

测量和态的叠加

宇称算子，投影算子理论

量子力学假设

假设1：波函数假设

假设2：量子力学算子

假设3：本征值

假设4：本征函数完备集

假设5：平均值假设

假设6：含时薛定谔方程

假设7：电子自旋

假设8：泡利不相容原理

6 近似方法

7 电子自旋和泡利原理

现代量子化学部分

8 多电子波函数和算子

9 Hartree-Fock近似和从头计算法

10 量子化学高级计算方法

11 化学问题的理论计算研究

机器学习

Recent advances and applications of machine learning in solid-state materials science

第一性原理计算

Electronic-structure methods for materials design
Ab initio molecular dynamics: Concepts, recent developments, and future trends
Discovering and understanding materials through computation

Metadynamics

Using metadynamics to explore complex free-energy landscapes
Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations

地球化学与元素分配

CALYPSO晶体结构预测

CALYPSO: A method for crystal structure prediction
Interface structure prediction via CALYPSO method

其他

Materials discovery at high pressures

1993 Free energy calculations- Applications to chemical and biochemical phenomena.pdf
2002 Escaping free-energy minima.pdf
2006 Crystal structure transformations in SiO2 from classical and ab initio metadynamics.pdf
2008 Metadynamics- a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science.pdf
2008 Well-Tempered Metadynamics-A Smoothly Converging and Tunable Free-Energy Method.pdf
2009 Basic ingredients of free energy calculations A review.pdf
2011 Metadynamics.pdf
2012 New advances in metadynamics.pdf
2013 From Metadynamics to Dynamics.pdf
2014 Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration.pdf
2016 Crystal Nucleation in Liquids- Open Questions and Future Challenges in Molecular Dynamics Simulations.pdf
2016 蛋白质结构变化的多尺度模拟及控制机制分析_韩孟之.pdf
2018_Molecular dynamics simulations of liquid silica crystallization.pdf
2020 Pathways for the formation of ice polymorphs from water predicted by a metadynamics method.pdf
2020_Using metadynamics to explore complex free-energy landscapes.pdf
2021 Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials.pdf

2016 蛋白质结构变化的多尺度模拟及控制机制分析_韩孟之.pdf
CuZr非晶合金玻璃形成能力、塑性变形单元及晶化问题的原子尺度理论研究.pdf
Na-,K-,Ca-蒙脫石分子動力學與元動力學模擬_江舒棋.pdf
亚硝酸和硝酸气液界面形成及有机物参与成核机理的理论研究.pdf
使用Harmonic Linear Discriminant Analysis方法研究多个柔性蛋白质区域的构象变化.pdf
固有无序蛋白结构系综的分子动力学模拟研究.pdf
基于Metadynamic的分子动力学模拟建立受体活性区分模型.pdf
基于深度张量神经网络理论预测氨基酸能量.pdf
底物-产物相互作用在腺苷酸激酶功能运动中的作用研究.pdf
机器学习势方法及其在结构预测与相变模拟中的应用.pdf
核酸分子折叠机制的模拟研究.pdf
电化学反应的理论模拟恒电位自由能的计算方法与应用.pdf
电子输运与表面反应过程中的环境效应的第一性原理研究.pdf
硅基材料网络结构的计算机模拟.pdf
纳米体系和半导体能源材料的理论研究.pdf
蛋白质巯基亚硝基化反应机理的理论研究.pdf
量子力学和分子力学结合方法在酶催化研究中的应用.pdf
金属离子调控的蛋白质多尺度构象运动.pdf

1969 MINIMIZATION OF POLYPEPTIDE ENERGY, VIII. APPLICATION OF THE DEFLATION TECHNIQUE TO A DIPEPTIDE.pdf
1985 The Tunneling Algorithm for the Global Minimization of Functions.pdf
1989 Tabu Search—Part I.pdf
1994 Local elevation A method for improving the searching properties of molecular dynamics simulation.pdf
1995 Predicting slow structural transitions in macromolecular systems Conformational flooding.pdf
1996 A method to calculate the probability distribution for systems with large energy barriers.pdf
2001 Calculating free energies using average force.pdf
2002 Escaping free-energy minima.pdf
2005 Flexible Docking in Solution Using Metadynamics.pdf
2006 Efficient Reconstruction of Complex Free Energy Landscapes by Multiple Walkers Metadynamics.pdf
2006 Free-Energy Landscape for β Hairpin Folding from Combined Parallel Tempering and Metadynamics.pdf
2006 Metadynamics as a Tool for Exploring Free Energy Landscapes of Chemical Reactions.pdf
2007 A Bias-Exchange Approach to Protein Folding.pdf
2007 Metadynamics in Essential Coordinates Free Energy Simulation of Conformational Changes.pdf
2008 Dissociation of minor groove binders from DNA insights from metadynamics simulations.pdf
2008 Free Energies from Adaptive Reaction Coordinate Forces (FEARCF) an application to ring puckering.pdf
2008 Stabilization of resonance states by an asymptotic Coulomb potential.pdf
2008 Well-Tempered Metadynamics A Smoothly Converging and Tunable Free-Energy Method.pdf
2009 PLUMED A portable plugin for free-energy calculations with molecular dynamics.pdf
2009 Simulation of structural phase transitions by metadynamics.pdf
2009 Using the local elevation method to construct optimized umbrella sampling potentials Calculation of the relative free energies and interconversion barriers of glucopyranose ring conformers in water.pdf
2010Markov Chain Monte Carlo Method without Detailed Balance.pdf
2011 An infinite swapping approach to the rare-event sampling problem.pdf
2011 Approaching a parameter-free metadynamics.pdf
2012 Metadynamics with Adaptive Gaussians.pdf
2013 Demonstrating the Transferability and the Descriptive Power of Sketch-Map.pdf
2013 Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables.pdf
2013 Using collective variables to drive molecular dynamics simulations.pdf
2014 Endohedral confinement of a DNA dodecamer onto pristine carbon nanotubes and the stability of the canonical B form.pdf
2014 Molecular Dynamics in the Multicanonical Ensemble Equivalence of Wang–Landau Sampling, Statistical Temperature Molecular Dynamics, and Metadynamics.pdf
2014 PLUMED 2 New feathers for an old bird.pdf
2015 Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering.pdf
2015 Replica state exchange metadynamics for improving the convergence of free energy estimates.pdf
2015 The Adaptive Biasing Force Method Everything You Always Wanted To Know but Were Afraid To Ask.pdf
2016 Conformational Thermodynamics of DNA Strands in Hydrophilic Nanopores.pdf
2017 Neural Network and Nearest Neighbor Algorithms for Enhancing Sampling of Molecular Dynamics.pdf
2017 Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces.pdf
2018 Learning free energy landscapes using artificial neural networks.pdf
2018 Reinforced dynamics for enhanced sampling in large atomic and molecular systems.pdf
关于超级计算发展战略方向的思考_葛蔚

2017 “Molecular Insight into CO2 “Trapdoor” Adsorption in Zeolite Na-RHO”
2010 Historical Perspective and Current Outlook for Molecular Dynamics As a Chemical Engineering Tool
2022 Molecular simulations: past, present, and future (a Topical Issue in EPJB)
Dynamic Acid/Base Equilibrium in Single Component Switchable Ionic Liquids and Consequences on Viscosity
2021 Ab Initio Molecular Dynamics Simulations of the SN1/SN2 Mechanistic Continuum in Glycosylation Reactions

2015 First-principles investigation of the dissociation and coupling of methane on small copper clusters: Interplay of collision dynamics and geometric and electronic effects
2016 Proton transfer from water to anion: Free energy profile from first principles metadynamics simulations
2021 Exploring the crystallization path of lithium disilicate through metadynamics simulations

1: 10.1073/pnas.202427399
2: 10.1063/1.2821102
3: 10.1080/00268970902852608
4: 10.1002/jcc.21253
5: 10.1103/PhysRevLett.100.020603
6: 10.1103/PhysRevE.84.037701
7: 10.1021/ct500077d
8: 10.1073/pnas.64.1.42
9: 10.1137/0906002
10: 10.1287/ijoc.1.3.190
11: 10.1007/BF00124016
12: 10.1103/PhysRevE.52.2893
13: 10.1016/S0301-0104（96）00247-9
14: 10.1063/1.1410978
15: 10.1021/ct3002464
16: 10.1021/jp068587c
17: 10.1021/ct3010563
18: 10.1063/1.4830403
19: 10.1021/jp054359r
20: 10.1021/ja062463w
21: 10.1021/jp067873l
22: 10.1021/ct5009087
23: 10.1063/1.3643325
24: 10.1103/PhysRevLett.105.120603
25: 10.1002/jcc.23945
26: 10.1021/acs.jctc.7b00188
27: “学习高维自由能表面的随机神经网络方法”
28: 10.1103/PhysRevLett.119.150601
29: 10.1063/1.5019675
30: 10.1021/jp506633n
31: 10.1063/1.5018708
32: 10.1021/ar040198i
33: 10.1021/ja0445950
34: 10.1093/nar/gkn561
35: 10.1524/zkri.220.5.489.65078
36: 10.1063/1.4881422
37: 10.1021/acs.jpcc.6b06234
38: 10.1016/j.cpc.2009.05.011
39: 10.1016/j.cpc.2013.09.018
40: “使用集体变量来驱动分子动力学模拟”
41: 10.1080/00268976.2013.813594

1: 这个Python游戏库，打开就能玩一天！
2: 介绍一款能取代 Scrapy 的爬虫框架 - feapder
3: 20 个关于程序员的笑话，看懂了，你就不会笑了，也不会羡慕他们工资高了！
4: Python | 细数知乎上值得关注的Python大佬
5: 68 个 Python 内置函数详解
6: 15 个让新手爱不释手的 Python 高级库
7: 用 Markdown 做的 PPT，真的太强了！
8: 零代码爬虫神器 – Web Scraper 的使用
9: 花了两天，终于把 Python 的 setup.py 给整明白了
10: 推荐一款超棒的抓包分析工具 - Burp Suite
11: 分享几段祖传的 Python 代码，拿来直接使用！
12: DockerHub上最受欢迎的151个官方镜像，相传掌握第17个可以主宰宇宙！
13: 秀！这应该是最好的 “re正则表达式” 使用教程了
14: 卧槽！我写的 Docker 镜像下载量居然突破了一百万…
15: 用Python解决高数所有计算题–sympy求解极限、积分、微分、二元一次方程等
16: 30 个Python代码实现的常用功能（附案例源码）
17: 吐血整理！140种Python标准库、第三方库和外部工具都有了

D:.
│ 12076639_计算化学实验_fenleiID0O6000_p198.pdf
│ 1_传递过程原理及应用(查金荣).pdf
│ Periodic_table-2014.png
│ Thermal_Physics_by_CHARLES_KITTEL_and_HE.pdf
│ Transport Phenomena 2nd edition.pdf
│ 《结构化学基础》第四版.pdf
│ 中兴大学_Modern Physics for Scientists and Engineers-Brooks Cole (2012)-Stephen T. Thornton, Andrew Rex - .pdf
│ 冶金工程专业实验指导书.pdf
│ 冶金熔体.pdf
│ 国科大量子力学考试大纲.pdf
│ 大学物理学(张三慧).pdf
│ 应用催化基础-吴越_2009.pdf
│ 样章 高分子链构象统计学.PDF
│ 物理学史(郭奕玲).pdf
│ 目录.pdf
│ 硅酸盐结构化学 结构、成键和分类.pdf
│ 精细化工生产流程图解.pdf
│ 选矿与冶金药剂分子设计.pdf
│ 邢其毅-基础有机化学上下册（第三版）.pdf
│ 金属熔体结构—边秀房—上海交通大学出版社.pdf
│ 高纯金属材料-郭学益.pdf
│
├─Linux
│ Linux Command Line and Shell Scripting Bible, 3rd Edition.pdf
│ Linux Shell脚本攻略.第3版.pdf
│ linux从入门到精通(第2版).pdf
│ Linux命令行与shell脚本编程大全.第3版.pdf
│ Linux命令行大全.pdf
│ Linux就该这么学.pdf
│
├─matlab书籍
│ MATLAB与数学实验+第2版_13569348.pdf
│ MATLAB在数学建模中的应用_卓金武(上).pdf
│ MATLAB在数学建模中的应用_卓金武(下).pdf
│ MATLAB神经网络43个案例分析_13353536.pdf
│ MATLAB统计分析与应用：40个案例分析 第2版_13820477.pdf
│
├─python
│ │ 0000.txt
│ │ Django 基础教程 (Leif Azzopardi David Maxwell) (z-lib.org).pdf
│ │ Docker容器与容器云（第2版） (Unknown) (z-lib.org).pdf
│ │ Flask Web开发：基于Python的Web应用开发实战 ([美] Miguel Grinberg) (z-lib.org).pdf
│ │ Math Adventures with Python An Illustrated Guide to Exploring Math with Code (Peter Farrell) (z-lib.org).pdf
│ │ PyCharm 中文指南 (it-ebooks) (z-lib.org).epub
│ │ Python 3标准库 (道格·赫尔曼(Doug Hellmann)) (z-lib.org).pdf
│ │ Python Cookbook（第3版）中文版 ( etc.) (z-lib.org).pdf
│ │ Python for Kids A Playful Introduction to Programming (Jason R. Briggs) (z-lib.org).pdf
│ │ python基础教程（第三版） ([挪] Magnus Lie Hetland 著 袁国忠 译) (z-lib.org).pdf
│ │ Python学习手册 原书第5版 下册 (马克·卢茨) (z-lib.org).pdf
│ │ python学习手册（原书第5版）上册 (马克·卢茨) (z-lib.org).pdf
│ │ Python数据分析基础 (Brownley, Clinton) (z-lib.org).pdf
│ │ Python数据科学手册 (Jake VanderPlas) (z-lib.org).pdf
│ │ Python极客项目编程 ((美)Mahesh Venkitachalam) (z-lib.org).pdf
│ │ Python核心编程 (Wesley Chun) (z-lib.org).pdf
│ │ Python编程 从入门到实践 = Python Crash Course (Eric Matthes) (z-lib.org).pdf
│ │ Python编程入门（第3版） (Toby Donaldson) (z-lib.org).pdf
│ │ Python编程导论.第2版 (图灵程序设计丛书) (z-lib.org).pdf
│ │ Python编程快速上手—让繁琐工作自动化(Automate the boring stuff with python) (Al, Sweigart 王海鹏) (z-lib.org).pdf
│ │ Python编程时光 (it-ebooks) (z-lib.org).epub
│ │ Python计算机视觉编程 ([美] Jan Erik Solem) (z-lib.org).pdf
│ │ Python语言及其应用 (Bill Lubanovic) (z-lib.org).pdf
│ │ Python高性能编程【文字版】 ([美] Micha Gorelick 戈雷利克 Ian Ozsvald 欧日沃尔德) (z-lib.org).pdf
│ │ R数据科学 ( etc.) (z-lib.org).pdf
│ │ Think Python 2e 中文版 (wizardforcel) (z-lib.org).pdf
│ │ [图灵程序设计丛书]Python科学计算最佳实践：SciPy指南 ([澳]伊格莱西亚斯) (z-lib.org).pdf
│ │ [图灵程序设计丛书]代码之外的功夫程序员精进之路【高清文字版】 (Gregory T.Brown) (z-lib.org).pdf
│ │ 利用Python进行数据分析 原书第2版 (Wes McKinney) (z-lib.org).pdf
│ │ 图灵程序设计丛书：Python 高手进阶之路（套装全10册） ( etc.) (z-lib.org).pdf
│ │ 安卓Frida逆向与抓包实战 (陈佳林) (z-lib.org).pdf
│ │ 数据科学入门 (数据科学入门) (z-lib.org).pdf
│ │ 数据科学实战 (Rachel Schutt, Cathy O’Neil) (z-lib.org).pdf
│ │ 流畅的Python = Fluent Python clear, concise, and effective programming (Luciano Ramalho) (z-lib.org).pdf
│ │ 父与子的编程之旅 (Warren Sande, Carter Sande) (z-lib.org).pdf
│ │ 用Python学数学-2021 ([美] 彼得 • 法雷尔（Peter Farrell）) (z-lib.org).pdf
│ │ 编程之美 (《编程之美》小组) (z-lib.org).pdf
│ │ 零基础入门学习Python 第2版 (小甲鱼) (z-lib.org).pdf
│ │
│ ├─C语言
│ │ [图灵程序设计丛书]C语言程序设计：现代方法（第2版）【文字版】 ([美] K. N. King) (z-lib.org).pdf
│ │ 零基础入门学习C语言——带你学C带你飞（微课视频版） (小甲鱼) (z-lib.org).pdf
│ │
│ ├─EQUB格式
│ │ Python数据处理 ( etc.) (z-lib.org).epub
│ │ Python数据科学与机器学习：从入门到实践 ([美] 弗兰克 • 凯恩 [[美] 弗兰克 • 凯恩]) (z-lib.org).epub
│ │ Python数据科学入门 (德米特里·齐诺维耶夫(Dmitry Zinoviev)) (z-lib.org).epub
│ │ Python机器学习 (数据科学与工程技术丛书) (塞巴斯蒂安·拉施卡（Sebastian Raschka）) (z-lib.org).epub
│ │ Python机器学习手册：从数据预处理到深度学习 (韩慧昌 林然 等) (z-lib.org).epub
│ │ Python机器学习：手把手教你掌握150个精彩案例：微课视频版 (柯博文) (z-lib.org).epub
│ │ 像计算机科学家一样思考Python（第2版）（异步图书） (艾伦 B. 唐尼 (Allen B. Downey)) (z-lib.org).azw3
│ │ 图灵程序设计丛书：Java进阶高手（套装全8册 Java 8函数式编程 Java技术手册（第6版） Java性能权威指南 Java编程思维 Java攻略：Java常见问题的简单解法 精通Java并发编程（第2版） Java实战（第2版）… ( etc.) (z-lib.org).epub
│ │ 机器学习算法的数学解析与Python实现 (智能系统与技术丛书) (莫凡) (z-lib.org).epub
│ │ 深入浅出Python机器学习 (段小手) (z-lib.org).cbr
│ │
│ ├─Go
│ │ Go语言圣经 (yar999) (z-lib.org).pdf
│ │ Go语言高级编程 (chai2010) (z-lib.org).pdf
│ │
│ ├─HTML
│ │ HTML CSS JAVASCRIPT网页制作从入门到精通 第3版 (刘西杰，张婷著) (z-lib.org).pdf
│ │ HTML与CSS入门经典(第7版).pdf (HTML与CSS入门经典(第7版).pdf) (z-lib.org).pdf
│ │
│ ├─Java
│ │ Java程序员修炼之道 (图灵程序设计丛书 79) ( etc.) (z-lib.org).pdf
│ │ [图灵程序设计丛书]Java攻略 Java常见问题的简单解法【文字版】 (Ken Kousen) (z-lib.org).pdf
│ │
│ ├─JavaScript
│ │ JavaScript函数式编程 (（美）佛格斯著 [（美）佛格斯著]) (z-lib.org).pdf
│ │ JavaScript权威指南(第6版) (David Flanagan) (z-lib.org).pdf
│ │ JavaScript高级程序设计（第4版） (Matt Frisbie) (z-lib.org).pdf
│ │ [图灵程序设计丛书]你不知道的JavaScript(上卷) (Kyle Simpson) (z-lib.org).pdf
│ │
│ ├─Linux
│ │ Linux命令行与shell脚本编程大全.第3版 (布鲁姆，布雷斯纳汉) (z-lib.org).pdf
│ │ 深入理解Linux内核（中文第3版）.pdf (深入理解Linux内核（中文第3版）.pdf) (z-lib.org).pdf
│ │ 鸟哥的 Linux 私房菜：基础学习篇 第四版 (wizardforcel) (z-lib.org).pdf
│ │
│ ├─SQL
│ │ SQL学习指南 ([美]博利厄（Alan Beaulieu）, 张伟超, 林青松) (z-lib.org).pdf
│ │ SQL必知必会 (Ben Forta) (z-lib.org).pdf
│ │ SQL必知必会（第5版） (福达) (z-lib.org).pdf
│ │
│ ├─数学基础
│ │ 数学之美（第三版） (吴军) (z-lib.org).pdf
│ │ 程序员的数学 (结城浩) (z-lib.org).pdf
│ │ 统计思维：程序员数学之概率统计 (Allen B.Downey) (z-lib.org).pdf
│ │
│ ├─数据抓取与可视化
│ │ C Primer Plus 第6版 中文版 ([美]史蒂芬·普拉达（Stephen Prata） [Prata） etc.) (z-lib.org).pdf
│ │ Python数据可视化 (黑马程序员) (z-lib.org).pdf
│ │ Python数据可视化之matplotlib实践 (刘大成) (z-lib.org).mobi
│ │ Python数据可视化编程实战 第2版 (伊戈尔·米洛瓦诺维奇 迪米特里·富雷斯 朱塞佩·韦蒂格利 颛清山) (z-lib.org).pdf
│ │ Python数据抓取技术与实战 (潘庆和，赵星驰) (z-lib.org).pdf
│ │ Python程序设计现代方法 (黑马程序员) (z-lib.org).pdf
│ │ 深入浅出Pandas：利用Python进行数据处理与分析 (李庆辉) (z-lib.org).pdf
│ │
│ ├─机器学习
│ │ NLTK基础教程 用NLTK和Python库构建机器学习应用 ([印度] Nitin Hardeniya 哈登尼亚) (z-lib.org).pdf
│ │ Python数据挖掘入门与实践 (张良均等) (z-lib.org).pdf
│ │ Python机器学习及实践——从零开始通往Kaggle竞赛之路 (范淼，李超编著) (z-lib.org).pdf
│ │ Python机器学习基础教程 ([德]Andreas C. Müller,[美]Sarah Guido) (z-lib.org).pdf
│ │ Python机器学习经典实例 (图灵程序设计丛书) (z-lib.org).pdf
│ │ Python机器学习：预测分析核心算法 (公众号：小楼杂谈) (z-lib.org).pdf
│ │ Python极简讲义：一本书入门数据分析与机器学习(纠斜+书签) (张玉宏) (z-lib.org).pdf
│ │ Python深度学习 (Francois Chollet) (z-lib.org).pdf
│ │ Python深度学习入门 从零构建CNN和RNN-2021 ([美] 塞思·韦德曼译者：郑天民) (z-lib.org).pdf
│ │ [图灵程序设计丛书].深度学习的数学 ([图灵程序设计丛书].深度学习的数学) (z-lib.org).pdf
│ │ [图灵程序设计丛书]精通特征工程【文字版】 (爱丽丝•郑 阿曼达•卡萨丽) (z-lib.org).pdf
│ │ 图解深度学习 (山下隆义) (z-lib.org).pdf
│ │ 实用机器学习.pdf (实用机器学习.pdf) (z-lib.org).pdf
│ │ 机器学习 (周志华) (z-lib.org).pdf
│ │ 机器学习实战 = Machine Learning in Action (Peter Harrington) (z-lib.org).pdf
│ │ 机器学习导论.pdf (机器学习导论.pdf) (z-lib.org).pdf
│ │ 机器学习导论（原书第3版） ([土耳其] 埃塞姆·阿培丁（EthemAlpaydin）) (z-lib.org).pdf
│ │ 深度学习 ( etc.) (z-lib.org).pdf
│ │ 深度学习入门：基于Python的理论与实现 (斋藤康毅) (z-lib.org).pdf
│ │ 深度学习基础与实践 ([美]乔希·帕特森、[美]亚当·吉布森) (z-lib.org).pdf
│ │ 深度学习进阶：自然语言处理 (斋藤康毅) (z-lib.org).pdf
│ │ 白话机器学习的数学 (立石贤吾) (z-lib.org).pdf
│ │ 百面机器学习 算法工程师带你去面试 (葫芦娃) (z-lib.org).pdf
│ │ 跟着迪哥学Python数据分析与机器学习实战 (唐宇迪) (z-lib.org).pdf
│ │
│ ├─正则表达式
│ │ 正则表达式必知必会 (Ben Forta) (z-lib.org).pdf
│ │ 精通正则表达式 第3版 (（美）佛瑞德（Friedl, J.E.F.）著 余晟译) (z-lib.org).pdf
│ │
│ ├─渗透测试编程
│ │ Python渗透测试编程技术 方法与实践(第2版) (李华峰) (z-lib.org).pdf
│ │ python黑帽子：黑客与渗透测试编程之道 ([美]Justin Seitz 著 孙松柏 李聪 润秋 译) (z-lib.org).pdf
│ │ 有趣的二进制 软件安全与逆向分析 (图灵程序设计丛书) ([日]爱甲健二) (z-lib.org).pdf
│ │ 黑客攻防技术宝典Web实战篇(第2版) (图灵程序设计丛书•网络安全系列) (Dafydd Stuttard) (z-lib.org).pdf
│ │
│ ├─爬虫
│ │ HTTP抓包实战 (肖佳) (z-lib.org).pdf
│ │ Python 3反爬虫原理与绕过实战 (韦世东) (z-lib.org).epub
│ │ Python 3网络爬虫开发实战 (崔庆才) (z-lib.org).pdf
│ │ Python 3网络爬虫开发实战(图灵出品) (崔庆才) (崔庆才) (z-lib.org).pdf
│ │ Python 项目案例开发从入门到实战：爬虫、游戏和机器学习 (郑秋生, 夏敏捷) (z-lib.org).pdf
│ │ Python3网络爬虫宝典 (韦世东) (z-lib.org).azw3
│ │ Python快乐编程——网络爬虫 (千锋教育高教产品研发部) (z-lib.org).pdf
│ │ python爬虫开发与项目实战 (佚名) (z-lib.org).pdf
│ │ Python爬虫开发与项目实战 (范传辉) (z-lib.org).epub
│ │ Python程序设计基础（第2版） (董付国) (z-lib.org).pdf
│ │ Python网络数据采集 (Ryan Mitchell, 陶俊杰, 陈小莉) (z-lib.org).pdf
│ │ Python网络爬虫权威指南（第 2 版） (米切尔) (z-lib.org).pdf
│ │ Scrapy网络爬虫实战 (东郭大猫) (z-lib.org).pdf
│ │ 从零开始学Python网络爬虫 (罗攀 蒋仟) (z-lib.org).pdf
│ │ 用 Python 写网络爬虫 (Katharine Jarmu,Richard Lawson) (z-lib.org).pdf
│ │ 精通Python爬虫框架Scrapy（异步图书） (迪米特里奥斯·考奇斯·劳卡斯) (z-lib.org).azw3
│ │ 精通Python网络爬虫：核心技术、框架与项目实战 (韦玮) (z-lib.org).epub
│ │ 精通Scrapy网络爬虫 (刘硕) (z-lib.org).azw3
│ │ 自己动手写网络爬虫 (罗刚, 王振东) (z-lib.org).pdf
│ │
│ ├─算法和数据结构
│ │ -学习JavaScript数据结构与算法第三版 Learning JavaScript Data Structures and Algorithms 3rd Edition ([巴西]格罗纳（LoianeGroner）, 孙晓博, 邓钢, 吴双, 陈迪, 袁源) (z-lib.org).pdf
│ │ Python数据结构与算法分析（第2版） (Bradley N. Miller, David L. Ranum) (z-lib.org).pdf
│ │ 啊哈算法 (啊哈磊) (z-lib.org).pdf
│ │ 我的第一本算法书 (石田保辉 宫崎修一) (z-lib.org).pdf
│ │ 数学与泛型编程 高效编程的奥秘 ( etc.) (z-lib.org).pdf
│ │ 数据结构与算法图解 ([美]杰伊•温格罗，袁志鹏译) (z-lib.org).pdf
│ │ 数据结构与算法：Python语言描述 (裘宗燕) (z-lib.org).pdf
│ │ 漫画算法-小灰的算法之旅(Python篇) (魏梦舒) (z-lib.org).pdf
│ │ 白话机器学习算法 ([新加坡] 黄莉婷,苏川集,武传海(译)) (z-lib.org).pdf
│ │ 算法 第四版 Algorithms (4th Edition)(Chinese Edition) ((美)Robert Sedgewick，(美)Kevin Wayne著 谢路云译) (z-lib.org).pdf
│ │ 算法之美：指导工作与生活的算法 ([美] 布莱恩·克里斯汀、汤姆·格里菲思 [[美] 布莱恩·克里斯汀、汤姆·格里菲思]) (z-lib.org).pdf
│ │ 算法图解 (（美）巴尔加瓦（Aditya Bhargava）著, 袁国忠译) (z-lib.org).pdf
│ │ 算法导论 第三版 可复制 有目录 (Thmos.H.Cormen ,Charles E. Leiserson etc.) (z-lib.org).pdf
│ │ 算法心得：高效算法的奥秘（第2版） (Henry S. Warren, Jr. 爱飞翔[译]) (z-lib.org).pdf
│ │ 算法：C语言实现 (第1～4部分)基础知识、数据结构、排序及搜索 (塞奇威克) (z-lib.org).pdf
│ │ 计算机程序设计艺术（第一卷）：基本算法 (计算机程序设计艺术（第一卷）：基本算法) (z-lib.org).pdf
│ │ 问题求解：算法与数据结构（Python 版） (北京大学地空学院数据结构与算法课程 2015年及2016年上课同学) (z-lib.org).pdf
│ │
│ ├─英文原版
│ │ Mastering matplotlib A practical guide that takes you beyond the basics of matplotlib and gives solutions to plot complex data (Duncan M. McGreggor) (z-lib.org).pdf
│ │ Programming For Computations - Python A Gentle Introduction To Numerical Simulations With Python 3.6 (Svein Linge, Hans Petter Langtangen) (z-lib.org).pdf
│ │ SciPy and NumPy An Overview for Developers (Eli Bressert) (z-lib.org).pdf
│ │
│ └─计算机网络
│ 30天自制操作系统 (图灵程序设计丛书) (川合秀实) (z-lib.org).pdf
│ Andrew Tanenbaum & David Wetheral - 计算机网络 (第5版 扫描版) (2012) - libgen.li.pdf
│ HTTP权威指南 ( etc.) (z-lib.org).pdf
│ [“十二五”普通高等教育本科国家级规划教材] 谢希仁 - 计算机网络 7th(2017, 电子工业出版社) - libgen.lc.pdf
│ [图灵程序设计丛书]程序是怎样跑起来的 ([日]矢泽久雄,李逢俊) (z-lib.org).pdf
│ 图解HTTP (上野宣) (z-lib.org).pdf
│ 图解TCPIP（第五版） (乌尼日其其格) (z-lib.org).pdf
│ 图解服务器端网络架构 (图灵程序设计丛书) ([日]宫田宽士 著) (z-lib.org).pdf
│ 网络是怎样连接的 (户根勤) (z-lib.org).pdf
│
├─python与机器学习
│ MLP_机器学习实战高清PDF.pdf
│ ovito手册与总结.pdf
│ 【样书】《Python核心编程第3版中文版》.pdf
│ 机器学习_周志华.pdf
│
├─python书籍-淘宝
│ │ 01_利用Python进行数据分析 原书第2版.pdf
│ │ 03_python3标准库.pdf
│ │ 05_Python编程_从入门到实践.pdf
│ │ 06 Python编程快速上手.pdf
│ │ 09_Python机器学习基础教程.pdf
│ │ 10_Python学习手册(第4版).pdf
│ │ 11think p’yth’on.pdf
│ │ 12笨办法学p’yth’on第3版.pdf
│ │ 13简明P’yth’on教程.pdf
│ │ 14流畅的p’yth’on.pdf
│ │ 15零基礎學P’YTH’ON1-300.pdf
│ │ 15零基礎學P’YTH’ON301-467.pdf
│ │ 16p’yth’on项目开发案例集锦.pdf
│ │ 17Python基础教程（第3版）高清中文版.pdf
│ │ 2Head First P’yth’on（中文版）.pdf
│ │ 4P’yth’on3网络爬虫开发实战.pdf
│ │ 7P’yth’on从入门到项目实践（全彩版）.pdf
│ │ 8P’yth’on高级编程.pdf
│ │ Fortran_95_2003程序设计（第三版）高清扫描版.pdf
│ │ 趣学Python算法100例（详解100个趣味编程算法实例，为Python初学者打造，培养编程兴趣，拓宽编程思维,提高编程能力和算法设计能力） (刘河飞 闫凯峰) (z-lib.org).pdf
│ │
│ └─python_exercises+100
│ 01.py
│ 02.py
│ 04.py
│ 05.py
│
├─中外物理学精品书系
│ 数学物理方法专题-复变函数与积分变换 (吴崇试).pdf
│ 核磁共振成像——物理原理和方法 (俎栋林 高家红　著) .pdf
│ 特殊函数概论 (王竹溪，郭敦仁编著).pdf
│ 简明量子场论 (王正行).pdf
│ 量子色动力学专题 (黄涛) .pdf
│
├─冶金物理化学
│ 3_Metallurgical Thermodynamics and Kinetics.pdf
│ Metallurgical Thermodynamics & Kinetics.pdf
│ 冶金原理第2版李洪桂.pdf
│ 冶金熔体和溶液的计算热力学（张鉴）.pdf
│ 冶金物理化学—董元篪，王海川.pdf
│ 冶金物理化学张家芸高清版.pdf
│ 冶金物理化学教程-郭汉杰.pdf
│ 冶金物理化学简明教程第2版.pdf
│ 合金熔体热力学模型、预测值及其软件开发_丁学勇.pdf
│
├─凝聚态物理
│ 凝聚态物理学(上卷) (冯端 金国钧) (z-lib.org).pdf
│ 凝聚态物理学(下卷) (冯端 金国钧) (z-lib.org).pdf
│
├─原子物理学
│ 原子物理学-第四版-杨福家.pdf
│
├─吴大猷理论物理
│ 吴大猷理论物理（第一册）古典动力学 (吴大猷) (z-lib.org).pdf
│ 吴大猷理论物理（第七册）量子力学（乙部） (吴大猷) (z-lib.org).pdf
│ 吴大猷理论物理（第二册）量子论与原子结构 (吴大猷) (z-lib.org).pdf
│ 吴大猷理论物理（第五册）热力学、气体运动论及统计力学 (吴大猷) (z-lib.org).pdf
│ 吴大猷理论物理（第六册）量子物理（甲部） (吴大猷) (z-lib.org).pdf
│
├─固体物理习题解答
│ 《固体物理学习题解答》李延龄.pdf
│ 固体物理习题详解(配套Kittel固体物理导论) 吴代鸣主编 1983年 吉林人民出版社.pdf
│ 固体物理学【胡安 章维益】.pdf
│ 固体物理学习题指导.刘友之.pdf
│ 固体物理学及习题解答.徐至中.1989.pdf
│ 固体物理学问题和习题-黄波.pdf
│ 固体物理导论习题详解-吴代鸣.pdf
│ 固体物理概念题和习题指导 王矜奉.pdf
│ 固体物理概念题和习题指导-王矜奉-山大出版社.pdf
│ 固体物理（胡安）课后答案.PDF
│ 黄昆固体物理习题解答-完整版.pdf
│
├─固体物理学
│ (中外物理学精品书系) 阎守胜 - 固体物理基础-北京大学出版社 (2011).pdf
│ Introduction to Solid State Physics_kittel.pdf
│ 固体物理学 黄昆 1988_高清版.pdf
│ 固体物理学（黄昆）.pdf
│ 固体物理第二版费维栋.pdf
│ 固体理论 (李正中) (z-lib.org).pdf
│ 晶格动力学理论 (Unknown) .pdf
│ 黄昆固体物理.pdf
│ （超清晰版）固体物理导论 基泰尔 (C.KITTEL) (z-lib.org).pdf
│
├─大学本科教材
│ Gilbert Strang - Introduction to Linear Algebra Fifth Edition-WELLESLEY -CAMBRIDGE PRESS (2016 ).pdf
│ Introduction to the Thermodynamics of Materials 6th Edition.pdf
│ MATLAB与数学实验+第2版_13569348.pdf
│ [华章数学译丛] David C. Lay Steven R. Lay_ Judi J. McDonald - 线性代数及其应用（原书第5版） (2018, 机械工业出版社) - libgen.li.pdf
│ 同济大学数学系 - 高等数学. 下册-高等教育出版社 (2014).pdf
│ 同济大学数学系 - 高等数学·上册_ 第七版-高等教育出版社 (2014).pdf
│ 大学物理学 力学、电磁学第3版 (张三慧) (z-lib.org).pdf
│ 大学物理学 热学、光学、量子物理 (张三慧) (z-lib.org).pdf
│ 张三慧 - 大学物理学 学习辅导与习题解答-清华大学 (2009).pdf
│ 无机化学(大连理工,第5版).pdf
│ 无机化学(大连理工,第五版,高清版）.pdf
│ 材料科学基础 (余永宁) (z-lib.org).pdf
│ 物理化学 第五版上册(南大, 傅献彩).pdf
│ 物理化学 第五版下册(南大, 傅献彩).pdf
│ 线性代数 (申亚男 张晓丹 李为东) (z-lib.org).pdf
│ 高等数学 下册 第二版 (郑连存 胡志兴 王辉 朱婧) (z-lib.org).pdf
│ 高等数学（上）第二版 (苏永美，胡志兴) (z-lib.org).pdf
│
├─数学物理方法
│ 《数学物理方法》胡嗣柱 倪光炯.pdf
│ 《数学物理方法》第二版 胡嗣柱 倪光炯 著 课后习题答案 高等教育出版社.pdf
│ 函数论与泛函分析初步 (A.H.柯尔莫戈洛夫) (z-lib.org).pdf
│ 复分析导论：单复变函数 ([俄]沙巴特) (z-lib.org).pdf
│ 复变函数与积分变换 第5版 (李红) (z-lib.org).pdf
│ 数学物理方法 (梁昆淼) (z-lib.org).pdf
│ 数学物理方法 1 (柯朗（R.Courant），希伯尔特（D.Hilbert）) (z-lib.org).pdf
│ 数学物理方法（第三版） (吴崇试，高春媛) (z-lib.org).pdf
│ 数学物理方程的MATLAB解法与可视化 (Unknown) (z-lib.org).pdf
│ 物理学中的数学方法 (李政道) (z-lib.org).pdf
│ 第三版_变分法基础 (老大中) (z-lib.org).pdf
│ 第二版(变分法基础) 老大中 - 变分法基础.pdf
│ 顾樵_数学物理方法_p545_20220316_123831.pdf
│
├─朗道物理学教程系列
│ 1-朗道理论物理学教程 第一卷 力学(第五版) (Л. Д. 朗道, Е. М. 栗弗席兹 著, 李俊峰, 鞠国兴 译校) .pdf
│ 10-朗道理论物理学教程 第十卷 物理动理学(第二版) (Е. М. 栗弗席兹, Л. П. 皮塔耶夫斯基 著, 徐锡申,徐春华,黄京民 译) .pdf
│ 2-朗道理论物理学教程 第二卷 场论(第八版) (Л. Д. 朗道, Е. М. 栗弗席兹 著, 鲁欣,任朗,袁炳南 译,邹振隆 校) .pdf
│ 3-朗道理论物理学教程 第三卷 量子力学(非相对论理论)(第六版) (Л. Д. 朗道, Е. М. 栗弗席兹 著,严肃 译,喀兴林 校) .pdf
│ 4-朗道理论物理学教程 第四卷 量子电动力学(第四版) (别列斯基捷斯基 _ E. M. Lifshitz _ 栗弗席兹 _ 皮塔耶夫斯基 )-高等教育出版社 (2015).pdf
│ 5-朗道理论物理学教程 第五卷 统计物理学Ⅰ（第五版） (Л. Д. 朗道, Е. М. 栗弗席兹 著,束仁贵,束莼 译,郑伟谋 校) .pdf
│ 6-朗道理论物理学教程 第六卷 流体动力学(第五版) (Л. Д. 朗道, Е. М. 栗弗席兹 著,李植 译,陈国谦 审) .pdf
│ 7-朗道理论物理学教程 第七卷 弹性理论(第五版) (Л. Д. 朗道, Е. М. 栗弗席兹 著,武际可,刘寄星 译) .pdf
│ 8-朗道理论物理学教程 第八卷 连续介质电动力学（第四版） (Л. Д. 朗道, Е. М. 栗弗席兹 著,刘寄星,周奇 译).pdf
│ 9-朗道理论物理学教程 第九卷 统计物理学Ⅱ(凝聚态理论)(第四版) (9787040241600) .pdf
│ 新概念物理学教程 1力学 (赵凯华 罗蔚茵) (z-lib.org).pdf
│
├─格里菲斯
│ Griffiths量子力学概论学习指导与习题解答 (胡行, 贾瑜) (z-lib.org).pdf
│ 电动力学导论 (David. J. Griffiths) (z-lib.org).pdf
│ 粒子物理导论 格里菲斯（翻译版 原书第2版）.pdf
│ 量子力学概论（翻译版） (格里菲斯) (z-lib.org).pdf
│
├─泛函分析
│ (图灵数学·统计学丛书) Peter D. Lax - 泛函分析-人民邮电出版社 (2010).pdf
│ 实变函数与泛函分析基础 第4版 (程其襄 张奠宙 胡善文 薛以锋 编) .pdf
│ 泛函分析讲义 (许全华马涛尹智) .pdf
│
├─液态金属
│ (Cambridge Monographs on Mathematical Physics) Norman Henry March - Liquid Metals_ Concepts and Theory-Cambridge University Press (2005).pdf
│ (Chemistry Research and Applications) David K. Belashchenko - Liquid Metals_ From Atomistic Potentials to Properties, Shock Compression, Earth’s Core and Nanoclusters-Nova Science Publishers, Inc. (20.pdf
│ Iida T., Guthrie R.I.L. - The Physical Properties of Liquid Metals.djvu
│
├─热力学与统计物理
│ 化学应用统计力学.pdf
│ 北京大学物理化学丛书_统计热力学导论.pdf
│ 热力学与统计物理学 (林宗涵) (z-lib.org).pdf
│ 热力学统计物理 (汪志诚) (z-lib.org).pdf
│ 统计力学 李政道讲义 (李政道) (z-lib.org).pdf
│ 统计力学的基本原理 ([美]约西亚·威拉德·吉布斯 毛俊雯(译)) (z-lib.org).pdf
│ 统计力学（第三版） (R. K. Pathria Paul D. Beale 方锦清(译) 戴越(译)) (z-lib.org).pdf
│ 统计热力学 ([奥]E. 薛定谔 Erwin Schrodinger 徐锡申(译)) (z-lib.org).pdf
│ 统计热力学——梁希侠等编着.pdf
│
├─熔体界面热力学
│ │ 《硅酸盐物理化学》_.pdf
│ │ 冶金溶液热力学原理与计算.pdf
│ │ 冶金热力学_翟玉春.pdf
│ │ 冶金熔体_毛裕文.pdf
│ │ 冶金熔体和溶液的计算热力学_张鉴.pdf
│ │ 冶金熔体结构和性质的计算机模拟计算.pdf
│ │ 地球与行星科学中的热力学=Thermodynamics in Earth and Planetary Sciences_14155946.pdf
│ │ 熔融金属物理初步.pdf
│ │ 统计力学及其在物理化学中的应用.pdf
│ │ 非平衡态热力学和耗散结构.pdf
│ │ 高温熔体的界面物理化学.pdf
│ │
│ └─书籍
├─物理学大题典
│ 原子亚原子与相对论物理学 (杨保忠) (z-lib.org).pdf
│ 固体物理及物理量测量 (林鸿生 章世玲) (z-lib.org).pdf
│ 热学 热力学 统计物理 (郑久仁, 周子舫) (z-lib.org).pdf
│ 物理学大题典 力学（上册）（第二版） (强元棨，程稼夫，张鹏飞) (z-lib.org).pdf
│ 物理学大题典1 力学（下册）第2版 (强元棨，程稼夫，潘海俊) (z-lib.org).pdf
│ 量子力学 (张永德) (z-lib.org).pdf
│
├─理论力学
│ (理论力学) 梁昆淼 - 力学(理论力学部分).pdf
│ 力学 (舒幼生) (z-lib.org).pdf
│ 周衍柏 - 理论力学教程-高等教育出版社.pdf
│ 普通物理学教程力学 (漆安慎 杜婵英) (z-lib.org).pdf
│ 梁昆淼 力学 下 第4版.pdf
│ 梅凤翔 - 分析力学（上下）. 1,2-北京理工大学出版社 (2013-8).pdf
│ 理论力学 (刘川) (z-lib.org).pdf
│ 经典力学的数学方法 (В. И. 阿诺尔德 齐民友) (z-lib.org).pdf
│
├─电动力学
│ (物理学基础理论课程经典教材) 郭硕鸿 - 电动力学-高等教育出版社 (2008).pdf
│
├─电池类书籍
│ 锂离子电池用纳米硅及硅碳负极材料.pdf
│
├─离子液体
│ 离子液体性质、制备与应用-邓友全.pdf
│ 离子液体的应用及研究-张亚.pdf
│ 离子液体的性能及应用-王军.pdf
│ 绿色溶剂离子液体的应用与合成李汝雄.pdf
│
├─第一性原理计算
│ │ Ab Initio Molecular Dynamics Basic Theory and Advanced Methods (Marx D., Hutter J.) (z-lib.org).pdf
│ │ Computational Materials Science_ An Introduction, Second Edition-CRC Press (2017) Lee, June Gunn - .pdf
│ │ Computer Simulation of Liquids (Michael P. Allen, Dominic J. Tildesley) (z-lib.org).pdf
│ │ Density_Functional_Theory_A_Practical_Introduction.pdf
│ │ Electronic Structure -Basic theory and practical methods .pdf
│ │ Molecular Physical Chemistry A Computer-based Approach using Mathematica® and Gaussian by José J. C. Teixeira-Dias.pdf
│ │ QM-MM介绍(英文版).pdf
│ │ Richard M. Martin, Lucia Reining, David M. Ceperley - Interacting Electrons_ Theory and Computational Approaches-Cambridge University Press (2016).pdf
│ │ Roald Hoffman Solids and Surfaces A_Chemists View.pdf
│ │ Roald Hoffmann - Solids and Surfaces_ A Chemist’s View of Bonding in Extended Structures-Wiley-VCH (1989).pdf
│ │ VASPKIT—VASP软件预-后处理工具介绍.pdf
│ │ vasp入门指南-复旦-侯柱峰.pdf
│ │ VASP入门资料.rar
│ │ vasp官网例子.pdf
│ │ 《基于DFT的第一性原理计算方法简介》-姜俊.pdf
│ │ 《结构化学基础》习题解析 第四版.pdf
│ │ 《结构化学基础》第四版(1).pdf
│ │ 分子模拟-从算法到应用_.pdf
│ │ 分子模拟基础_李永健.pdf
│ │ 分子模拟的理论与实践_陈正隆.pdf
│ │ 固体量子化学英文版 [（俄）叶瓦列斯托夫 著] 2012年版.pdf
│ │ 密度泛函理论_李健.pdf
│ │ 密度泛函理论_胡英.pdf
│ │ 掺杂材料分子模拟与计算-张培新等-2012.pdf
│ │ 材料的设计、模拟与计算 CASTEP的原理及其应用_14648217.pdf
│ │ 理论化学原理与应用.[帅志刚，邵久书].pdf
│ │ 用电子结构方法探索化学第二版_摘录版.pdf
│ │ 电子结构理论与计算-李震宇.pdf
│ │ 福井谦一：化学反应与电子轨道.pdf
│ │ 计算化学_从理论化学到分子模拟_中科院+陈敏伯.pdf
│ │ 计算材料学-设计实践与方法.pdf
│ │ 计算材料学基础_张跃.pdf
│ │ 量化砖头——量子计算必须读懂的资料.pdf
│ │
│ ├─python
│ │ Learning Scientific Programming with Python_2020.pdf
│ │ 用Python做科学计算(scipy).pdf
│ │ 简明Python教程(重新排版打印版).pdf
│ │
│ ├─VASP入门资料
│ │ └─源资VASP课件
│ │ 源资培训班上机-VASP上机练习讲解 (MedeA-VASP相关模块MT-Phonon-Electronics-TSS).pdf
│ │ 源资培训班上机-VASP上机练习讲解 （MedeA-VASP模块）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（+U体系）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（MedeA-LAMMPS-GIBBS-MOPAC模块的使用）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（光学性质）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（几何结构优化-分子）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（几何结构优化-晶体）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（几何结构优化-表面、吸附）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（弹性常数）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（杂化泛函）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（电子结构计算-态密度）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（电子结构计算-电荷密度）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（电子结构计算-能带结构）.pdf
│ │ 源资培训班上机-VASP上机练习讲解（磁性体系）.pdf
│ │ 源资培训班课程-Linux命令操作.pdf
│ │ 源资培训班课程-MedeA建模.pdf
│ │ 源资培训班课程-VASP流程&输入文件.pdf
│ │ 源资培训班课程-VASP软件理论基础.pdf
│ │ 源资培训班课程-文献解读.pdf
│ │ 源资培训班课程-材料模拟计算介绍与应用.pdf
│ │
│ └─用户手册
│ Exploring Chemistry with Electronic Structure Methods (Third Edition 2015).pdf
│ Exploring Chemistry With Electronic Structure Methods(Second Edition).pdf
│ Gaussian用户手册_16版.pdf
│ lammps manual.pdf
│ vasp manual.2018.10.29.pdf
│
├─粒子物理学
│ 李政道讲义粒子物理和场论 (李政道) .pdf
│ 核物理与粒子物理 (孙汉城,寅新艺) .pdf
│ 粒子物理和薛定谔方程 ([奥]哈罗德·格罗斯 [法]安德烈·马丁 刘翔 等(译)) .pdf
│ 粒子物理学 (章乃森) (上册).pdf
│ 粒子物理学 (章乃森) (下册).pdf
│ 粒子物理学导论 (肖振军 吕才典) .pdf
│
├─量子力学
│ Alexei M. Tsvelik - Quantum field theory in condensed matter physics-Cambridge University Press (2007).pdf
│ David J. Griffiths - Introduction to Quantum Mechanics-Pearson Prentice Hall (2004).pdf
│ Errol G. Lewars (auth.) - Computational Chemistry_ Introduction to the Theory and Applications of Molecular and Quantum Mechanics-Springer International Publishing (2016).pdf
│ Hassani, Sadri - Mathematical Physics A Modern Introduction to Its Foundations-Springer International Publishing (2013).pdf
│ Michael P. Allen, Dominic J. Tildesley - Computer Simulation of Liquids-Oxford University Press (2017).pdf
│ Siegmund Brandt, Hans Dieter Dahmen (auth.) - The Picture Book of Quantum Mechanics-Springer-Verlag New York (2012).pdf
│ 曾谨言_量子力学卷1第5版_pg552.pdf
│ 曾谨言_量子力学卷2第5版_pg532.pdf
│ 量子力学与统计力学 (卢文发) .pdf
│ 顾樵_量子力学 Ⅰ＝QUANTUM MECHANICS Ⅰ_p318.pdf
│ 顾樵_量子力学II＝QUANTUM MECHANICS II_p621.pdf
│
└─量子化学
2014 Quantum Chemistry, 7ed (Ira N. Levine)-Solutions for the Exercices.pdf
2014 Quantum Chemistry, 7ed (Ira N. Levine).pdf
J. Michael Hollas - Modern spectroscopy (2004, J. Wiley) - libgen.lc.pdf
McQuarrie, Donald Allan - Quantum chemistry (2008, University Science Books) - libgen.lc.pdf
Modern Quantum Chemistry_ Introduction to Advanced Electronic Structure Theory-Dover Publications (1996)-Attila Szabo, Neil S. Ostlund - .pdf
Quantum Chemistry (7ed).pdf
Quantum States of Atoms and Molecules (Zielinksi et al.).pdf
中科院_量子化学教程_黄明宝儿.pdf
量子化学_基本原理和从头计算法_上.pdf
量子化学_基本原理和从头计算法_下.pdf
量子化学_基本原理和从头计算法_中.pdf

First-principle investigation of the structure and vibrational spectra of the local structures in LiF–BeF2 Molten Salts
The total time span of the AIMD simulation is 20 ps for gas phase clusters, and 30 ps for condensed phase at both temperatures. All AIMD simulations consist of two phases: An equilibration phase with temperature controlled through velocity rescaling, followed by production phase of 10 ps during which dynamics are strictly canonical. A recent report on the molten ZrO2 using similar AIMD setup suggests that 30 ps time span is able to reproduce experimental geometrical and dynamical properties satisfactorily [36].
To investigate the temperature effect on the vibrational structure, the AIMD simulation of the salt was carried out at 873 K followed by a subsequent simulation at 30 K. The higher temperature (873 K) is chosen to ensure the melting state of the salt, and the lower temperature is chosen to be super low to obtain clear and sharp vibrational spectra. The ionic temperatures were controlled using a NOSE-HOOVER scheme.

Composition-dependent microstructure evolution in liquid MgCl2-KCl: A first-principles molecular dynamics study
The initial configurations of 22.22, 33.33, 50.00 and 80.00 mol% MgCl2 with KCl were generated by packing ions randomly into given simulation cells using the Packmol code [26]. A series of simulation 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(see Fig. S1 in the supporting information). Then the high-temperature liquids were quenched at a rate of 180 K/ps to 1073 K. After these runs, the equilibrium volume was optimized at five fixed volumes for each particular composition. For each fixed volume, the total pressure (P) is evaluated as the average of the last 90% of a 6 ps simulation. Starting from the quenched liquids, 6 ps duration in FPMD is found to be long enough to reach the convergence of energy and pressure(Fig. S2). For each composition, the total pressures were fitted to a third-order Birch–Murnaghan equation of state [27,28] (Fig. S3), and the equilibrium volume was evaluated as the cell volume corresponding to zero pressure. Finally, FPMD simulation was carried out for 20 ps at this equilibrium volume for the following analysis of structure and transport properties. A time step of 1 fs was adopted to reduce the energy drift.

Ab Initio Molecular Dynamics of CdSe Quantum-Dot-Doped Glasses
The starting configuration for the glass matrix was generated by placing atoms randomly in a cubic simulation box. The total number of atoms for the glass was 540 (120 Na, 120 Si, and 300 O), with the simulation cell sizes (a = b = c = 19.383 Å, α = β = γ = 90°) kept constant throughout the simulation, giving a density consistent with experimental values (ρ = 2.492 g/cm3). (15) Hard constraints were imposed to avoid unphysically small interatomic distances. An initial classical molecular dynamics simulation was performed using a partial-charge rigid-ion pairwise potential developed by Pedone et al., (16) with the DL_POLY classic package. (24) The Coulomb interactions were calculated using the Ewald summation method (25) with a precision of 10–5 and a real-space cutoff for short-range interactions set to 7.6 Å. The Verlet algorithm was applied for the integration of the equations of motion with a time step of 1 fs. The glass structures were generated using a melt-quenching approach in the NVT ensemble at the target density from experimental data, using a Nosé–Hoover thermostat (26−28) with a relaxation time of 0.1 ps. The initial structure was heated up gradually in steps of 1000 K with a 60 ps MD run at each temperature from 300 to 6000 K. After equilibration of the liquid at 6000 K during 400 ps, the system was cooled gradually in steps of 500 K with a 60 ps MD run at each temperature from 6000 to 300 K. Another 200 ps NVT simulation was carried out at 300 K, together with a 200 ps NVE simulation in order to equilibrate the structure.

Modelling the local atomic structure of molybdenum in nuclear waste glasses with ab initio molecular dynamics simulations
The glass structures were generated using a melt-and-quench approach. The canonical ensemble (constant number of particles, volume and temperature or NVT) was applied and the Nosé–Hoover thermostat chain,30–32 with a relaxation constant 0.1 ps, was chosen to control the temperature fluctuations. For each composition, the initial configuration was heated up at 2300 K with a 25 ps AIMD run to ensure that the system was melted and well equilibrated at this temperature. Despite a small drift in the total energy the recorded energy fluctuations were lower than 0.001%. The molten structure was subsequently cooled using a stepwise process, consisting of a series of nine NVT AIMD runs of 10 ps each, with target temperatures set to 2000 K, 1800 K, 1600 K, 1400 K, 1200 K, 1000 K, 800 K, 600 K and 300 K. At 300 K the structure was further equilibrated for 10 ps, followed by a final AIMD production run of 10 ps, to collect the structural data. This computational scheme corresponds to a total simulation time of 135 ps and a nominal cooling rate of around 20 K ps−1. Cooling rates of this order of magnitude have been used in previous simulation studies, using AIMD,21,23–25,41,42 in order to prepare accurate structural models of glasses that are in agreement with experimental results.

Thermodynamics and structural properties of CaO: A molecular dynamics simulation study
In the general case, a first stage consists in equilibrating the system for 100 ps at the desired temperature, followed by a production run of 200 ps. For the temperature evolution of the properties in the solid state, a first simulation is run at 300 K and the subsequent higher temperatures were reached stepwise with a temperature step of 50 K at the end of each simulation. For the liquid state, before equilibration, a progressive heating stage is performed at 1012 K/s to 4000 K to get a fully melted configuration. Then, the system was cooled down stepwise with the same temperature step as for the solid branch, and we stopped this process when crystallization is observed. It should be mentioned that quenching runs were also carried out with various cooling rates ranging from 1011 K/s to 1013 K/s to observe the glass transition. However, the latter was never observed as the system crystallized during cooling around T = 2100 K, irrespective of the cooling rate.

A DFT-Based Aspherical Ion Model for Sodium Aluminosilicate Glasses and Melts
All MD calculations were performed in the NPT ensemble, where N is the particles number, P is the pressure, and T is the temperature. The studied compositions are summarized in Table 3 and Figure 1. The equations of motion were solved following the method by Martyna et al. where a Nose-Hoover chain thermostat was used for the temperature and pressure controls. (74) The time step was set to 0.5 fs. After starting from randomly distributed configurations with a simpler polarizable ion model (fitted via the same procedure as AIM), the simulation cell were carefully equilibrated with AIM under high temperature condition above 3500 K. Liquids were subsequently quenched by decreasing the temperature from 3500 to 300 K by steps of 100–500 K for 1.7 ns: the effective quenching rate is 1.9 K/ps. Three configurations were generated for each composition of sodium silicate and aluminosilicate. Additionally, in order to obtain smooth bond-angle distribution even in the case of minor bridging oxygen species, the simulation cells of sodium aluminosilicate glasses were enlarged to a 2 × 2 × 2 supercell in the quenching procedure at 1400 K. Coordination distances and numbers, and the fraction of bridging oxygen species, were determined running statistical averages of the configurations under the ambient condition.

Atomistic insight into the structure and diffusion properties of pollucite glass-ceramics
For the generation of glass, a stepwise cooling strategy with a timestep of 0.5 fs was performed: the system equilibrated in NPT ensemble for 100 ps and sampled every 150 timesteps in NVE ensemble for 60 ps at 4000, 3500, 3000, 2500, 2250, 2000, 1750, 1500, 1250, 300 K, respectively. For simulation of pollucite, a stepwise heating strategy was used: the temperatures were 300, 1000, 1500, 2000 K, respectively, which are below the melting point of pollucite. A cooling rate of 10 K/ps was utilized in this work, and it has been pointed that the cooling rate used in classical MD simulations is usually 1–10 K/ps [35,36]. The interface between pollucite and glass was also investigated. Firstly, the atoms were added to an orthorhombic box according to the previously obtained density to generate glass structure in NVT ensemble, and the box had a same size of pollucite (0 0 1) surface. The melt-quench strategy was the same as previous parameters. The initial distance between pollucite and glass was about 5 Å to avoid unreasonable structure [30]. Then the structure was fully relaxed at 1500 K in NPT ensemble for 200 ps under a pressure of 1 atm applied in the z direction [37]. Subsequently, the system was cooled down to 300 K. Finally, the final structure was obtained after equilibration for 200 ps.

Cooling rate dependence of the properties for Ti110Al14V4 alloy investigated by ab initio molecular dynamics
The Ti110Al14V4 configuration is heated to 2600 K, about 35.21% above the experimental value of 1923 K for the TC4 alloy, [27] to avoid the memory effect from the initial random structure. The molten alloy is then cooled stepwise down to 2400, 2200, 2000, 1800, 1600, 1400, 1200, 1000, and 800 K under two constant cooling rates of 1.33 × 1013 and 1015 K/s, denoted as v1 and v2, respectively. After quenching down to the desired temperature, the Ti110Al14V4 supercells are subjected to molecular dynamics simulations in the NpT ensemble with a constant number, pressure, and temperature [28]. The temperature is controlled by the Langevin thermostat with a friction coefficient of 2 ps−1 for all atoms and 10 ps−1 for the lattice degrees of freedom. A mass of 5 amu is used for the lattice degrees of freedom. A total of 5000 steps (15 ps) for the NpT ensemble simulations are performed, and the final 2000 steps are selected to estimate the equilibrium volume. Eventually, the calculated equilibrium volume is used as an input configuration in the following 10,000 steps of the NVT ensemble simulations (constant number, volume, and temperature) with a Nosé-Hoover thermostat controlling temperature [29]. The first 8000 steps are used to relax the system to reach thermal equilibrium, and the remaining 2000 AIMD steps are used to analyze the structural and kinetic properties.

Temperature-Dependent Properties of Molten Li2BeF4 Salt Using Ab Initio Molecular Dynamics
The heat bath temperature is controlled using a Nose thermostat. (74,75) A time step of 1 fs is chosen for the ionic motion integration. Based on the unit cell crystal structure with 126 atoms (Li = 36, Be = 18, and F = 72), we construct a cubic supercell containing 504 atoms (Li = 144, Be = 72, and F = 288), with periodic boundary conditions. The initial cubic supercell is heated to 2000 K within just 2 ps. The melt is further heated for 10 ps at this elevated temperature to fully eliminate the memory effect of the initial configuration. After this, the temperature of the system is lowered to 1500, 1000, 850, 750, 730, 700, 450, and finally to 300 K. At each temperature, the system is well-equilibrated for 10 ps to ensure it overcomes the diffusive stage and losses memory of atomic position and velocity history configuration from the previous structure. The velocity autocorrelation function (VACF) (see Supporting Information, Figure S1) ensures the loss of initial velocity from the previous configuration in the simulated model. It provides information on the dynamic motion of atoms with time. It shows that in the liquid model, the VACF dies out fast implying that the ions can leave the cage made up of surrounding ions more quickly. In a solid model, the VACF has more features and they die out much more slowly compared to melt which is due to the presence of more BeF4– tetrahedrons and the ordered structure of solid FLiBe.

Structural and dynamical properties of liquid Ag74Ge26 alloy studied by experiments and ab initio molecular dynamics simulation
The equilibrium volume at different temperatures was established by monitoring the pressure of system, in a criterion within 0.0 ± 1.0 kbar. A supercell containing 200 atoms (148 Ag atoms and 52 Ge atoms randomly distributed in a cubic box) was heated to 1200 K and relaxed for 4000 MD steps in order to remove the memory effects from the initial configuration and allow the system to reach the equilibrium liquid state at this temperature. Then the temperature was lowered to 1123, 1073, 1023, 976, 873, and 773 K by stages, at a cooling rate of 0.1 K/step. Each stage was relaxed more than 4000 steps, ensuring the persuasion for the equilibrium of system and reliability of simulations. Last 2000 steps were selected for statistical analyses. The structure factor, pair-correlation function, coordination numbers (CN), bond-angle distribution, Honeycutt and Andersen (HA) indices, Voronoi tessellation method and atomic cluster alignment method, were performed to analyze the atomic configurations.

Origin of short- and medium-range order in supercooled liquid Ge3Sb2Te6 using ab initio molecular dynamics simulations
The initial cubic cell consisted of 60 Ge, 40 Sb and 120 Te atoms. In line with our previous studies,11,12,20 the simulation cell was firstly kept at 2000 K for 30 ps to eliminate the memory effect. Secondly, the system was cooled down to 1273 K and fully relaxed to obtain an equilibrium liquid. Then, the liquid was gradually cooled down to each sampled temperature (1123 K, 1023 K, 923 K, 823 K, and 773 K) and eventually to 723 K with a cooling rate of 33.3 K ps−1. During the cooling process, the system was adjusted to make the pressure tend to zero. The length of the cubic box varied from 20.12 Å to 19.84 Å as the temperature decreased from 1273 K to 723 K. For each sample, it was relaxed for 6000 steps to collect the atomic trajectories, and the final configuration was regarded as the beginning of the next cooling process. Finally, these trajectories were utilized to analyze the evolution of local configurations in the fast cooling process.

Ab initio molecular dynamics simulation of binary Ni62.5Nb37.5 bulk metallic glass: validation of the cluster-plus-glue-atom model
Our simulation supercell contains 200 atoms, including 125 Ni atoms and 75 Nb atoms. The dimension of the cubic supercell was chosen as 13.62 Å, which is deduced from the measured mass density (9.40 g cm−1) of this glass at room temperature from our own experiment. The finite-size effect of the AIMD simulation supercell has been examined in previous studies [46, 47]. Initially, these Ni and Nb atoms were put in the cubic box randomly. The system was then melted at a high temperature of 1800 K for 20 ps to remove the memory effect from the initial configuration. After that, the system was gradually cooled down to 1000 K with a temperature interval of 200 K. From 1000 to 300 K, as “coming by” the critical temperature of forming BMG (the crystallization temperature T x and the glass transition temperature T g), a temperature interval of 100 K was adopted to reduce the annealing speed. At each temperature, the AIMD simulation last for 10 ps (or 5000 MD steps). The overall cooling rate is about 1.25 × 1013 K/s, which is comparable to those in previous AIMD simulations of various BMGs, but still much faster than the realistic cooling rate. At each temperature, 1000 configurations from the final MD runs were used to collect the averaged structural quantities, such as the partial pair-correlation functions (PCFs), distributions of coordination numbers (CNs), and local chemical environments for analysis.

Structure and dynamics of liquid Al1−xSix alloys by ab initio molecular dynamics simulations
We start the simulations with the atoms in random positions in a cubic supercell with periodic boundary conditions. The system was thermalized at 2000 K for 6 ps. This initial temperature is far above the melting point of the Al1−xSix alloys. We performed simulations at such a high temperature in order to avoid any memory effects from the initial configuration. Then the system was cooled down from 2000 K to 1573 K at a uniform cooling rate of 0.427 K/step for 3 ps. At this temperature, the structural and dynamical properties of liquid Al1−xSix were examined over an additional simulation time of 6 ps. The last 1000 time steps were used to analyze the properties of the samples. In order to see how sensitive is the structure and properties of the liquid alloys to the number of atoms in the supercell, we have performed simulations for liquid Al1−xSix at two different supercells. The small one contains 100 atoms, and the big one has 200 atoms. The simulation results show that the structure of the liquid is not sensitive to the size of the supercell. The changes in the pair correlation function and structure factor are very small. However, the diffusion constant shows some noticeable changes. Therefore, the size of the supercell will cause some error in the diffusion constant as will be shown in Section 3. Unless specified, the results presented in this paper are obtained from the simulations using the 200 atom unit cell.

150 “CrystalNets.jl: Identification of Crystal Topologies”
149 “Defective Nature of CdSe Quantum Dots Embedded in Inorganic Matrices”
148 “High-throughput computational screening of nanoporous materials in targeted applications”
147 “How Reproducible are Surface Areas Calculated from the BET Equation?”
146 “Chiral Lanthanum Metal-Organic Framework with Gated CO₂ Sorption and Concerted Framework Flexibility”
145 “Tunable acetylene sorption by flexible catenated metal–organic frameworks”
144 “Atomistic Models of Amorphous Metal−Organic Frameworks”
143 “Prediction of Thermal Properties of Zeolites through Machine Learning”
142 “Flexibility of a Metal–Organic Framework Enhances Gas Separation and Enables Quantum Sieving”
141 “Influence of Glass Composition on the Luminescence Mechanisms of CdSe Quantum-Dot-Doped Glasses”
140 ”Identification of a Grotthuss Proton Hopping Mechanism at Protonated Polyhedral Oligomeric Silsesquioxane (POSS)–Water Interface”
139 ”Emergence of Coupled Rotor Dynamics in Metal−Organic Frameworks via Tuned Steric Interactions”
138 ”Open questions on water confined in nanoporous materials”
137 ”Systematic Study of the Thermal Properties of Zeolitic Frameworks”
136 “MechElastic: A Python Library for Analysis of Mechanical and Elastic Properties of Bulk and 2D Materials”
135 “Best practices in machine learning for chemistry”
134 “Melting of hybrid organic–inorganic perovskites”
133 “The changing state of porous materials”
132 “Thermodynamic exploration of xenon/krypton separation based on a high-throughput screening”
131 “Transient Catenation in a Zirconium-Based Metal–Organic Framework and Its Effect on Mechanical Stability and Sorption Properties”
130
129
128 “Engineering micromechanics of soft porous crystals for negative gas adsorption”
127 “Machine learning approaches for the prediction of materials properties”
126 “Water Adsorption in Soft and Heterogeneous Nanopores”
125 “Isolating the Role of the Node-Linker Bond in the Compression of UiO-66 Metal–Organic Frameworks”
124 “The rise of preprints in chemistry”
123 “The role of temperature and adsorbate on negative gas adsorption in the mesoporous metal-organic framework DUT-49”
122 “Structure and chemistry of graphene oxide in liquid water from first principles”
121
120 “Ab Initio Molecular Dynamics of CdSe Quantum-Dot-Doped Glasses”
119 “Materials Databases: The Need for Open, Interoperable Databases with Standardized Data and Rich Metadata”
118 “Towards general network architecture design criteria for negative gas adsorption transitions in ultraporous frameworks”
117 “Systematic exploration of the mechanical properties of 13,621 inorganic compounds”
116 “Metal-organic framework crystal-glass composites”
115 “Structure, Dynamics and Thermodynamics of Intruded Electrolytes in ZIF-8”
114
113 “Rich Polymorphism of a Metal-Organic Framework in Pressure-Temperature Space”
112 “Correcting the Scientific Record: Retraction Practices in Chemistry and Materials Science”
111 “Mixed-metal metal–organic frameworks”
110 “MOF Decomposition and Introduction of Repairable Defects Using a Photodegradable Strut”
109 “Pressure promoted low-temperature melting of metal–organic frameworks”
108 “Soft Porous Crystals: Extraordinary Responses to Stimulation”
107 “Ab initio derived force fields for Zeolitic Imidazolate Frameworks: MOF-FF for ZIFs”
106 “Nanoscale Metamaterials: Meta-MOFs and Framework Materials with Anomalous Behavior”
105 “Modelling of framework materials at multiple scales: current practices and open questions”
104 “Rotational Dynamics of Linkers in Metal–Organic Frameworks”
103 “Impacts of the Imidazolate Linker Substitution (CH3, Cl or Br) on the Structural and Adsorptive Properties of ZIF-8”
102 “Emissive Azobenzenes Delivered on a Silver Coordination Polymer”
101 “Adsorption Contraction Mechanics: Understanding Breathing Energetics in Isoreticular Metal-Organic Frameworks”
100 “Negative Hydration Expansion in ZrW₂O₈: Microscopic Mechanism, Spaghetti Dynamics, and Negative Thermal Expansion”
99 “Conformational chiral polymorphism in cis-bis-triphenylphosphine complexes of transition metals”
98 “Structure and Dynamics of Water Confined in Imogolite Nanotubes”
97 “Air separation with graphene mediated by nanowindow-rim concerted motion”
96 “Structure and Dynamics of Solvated Polymers Near a Silica Surface: on the Different Roles Played by Solvent”
95 “Melting of Zeolitic Imidazolate Frameworks with Different Topologies: Insight from First-Principles Molecular Dynamics”
94 “On the use of the IAST method for gas separation studies in porous materials with gate-opening behavior”
93 Polycatenated 2D Hydrogen-Bonded Binary Supramolecular Organic Frameworks (SOFs) with Enhanced Gas Adsorption and Selectivity
92 “Forced intrusion of water and aqueous solutions in microporous materials: from fundamental thermodynamics to energy storage devices”
91 “Liquid metal-organic frameworks”
90 “Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning”
89 “Molecular Mechanism of Swing Effect in Zeolitic Imidazolate Framework ZIF-8: Continuous Deformation Upon Adsorption”
88 “Recent advances in the computational chemistry of soft porous crystals”
87 “Reproducible Research in Computational Chemistry of Materials”
86 “Macroscopic Simulation of Deformation in Soft Microporous Composites”
85 “Molecular Insight into CO2 “Trapdoor” Adsorption in Zeolite Na-RHO”
84 “Kinetic accessibility of porous materials adsorption sites studied through Lattice Boltzmann method”
83 “Interplay between defects, disorder and flexibility in metal-organic frameworks”
82
81 “Origins of Negative Gas Adsorption”
80 Heterometallic Metal–Organic Frameworks of MOF-5 and UiO-66 Families: Insight from Computational Chemistry
79 “Computational Chemistry Methods for Nanoporous Materials”
78 “Modelling photophysical properties of metal–organic frameworks: a density functional theory based approach”
77 “Microscopic Mechanism of Chiral Induction in a Metal–Organic Framework”
76 “ELATE: An open-source online application for analysis and visualization of elastic tensors”
75 “A pressure-amplifying framework material with negative gas adsorption transitions”
74 “Carbon dioxide transport in molten calcium carbonate occurs through an oxo-Grotthuss mechanism via a pyrocarbonate anion”
73
72 “Encoding complexity within supramolecular analogues of frustrated magnets”
71 “Non-Interpenetrated Metal–Organic Frameworks Based on Copper(II) Paddlewheel and Oligoparaxylene-Isophthalate Linkers: Synthesis, Structure, and Gas Adsorption”
70 “Controlled partial interpenetration in metal–organic frameworks”
69
68
67
66
65 “Defects and Disorder in Metal-Organic Frameworks”
64 “Adsorption deformation of microporous composites”
63
62
61
60 “Softening upon Adsorption in Microporous Materials: A Counterintuitive Mechanical Response”
59
58 “Computational characterization and prediction of metal-organic framework properties”

对一些论文中关于从头算和经典分子模拟的描述做一些笔记

Liquid–liquid transition and critical point in sulfur | Nature volume 584, pages382–386 (2020)
The liquid–liquid transition (LLT), in which a single-component liquid transforms into another one via a first-order phase transition, is an intriguing phenomenon that has changed our perception of the liquid state. LLTs have been predicted from computer simulations of water1,2, silicon3, carbon dioxide4, carbon5, hydrogen6 and nitrogen7. Experimental evidence has been found mostly in supercooled (that is, metastable) liquids such as Y2O3–Al2O3 mixtures8, water9 and other molecular liquids10,11,12. However, the LLT in supercooled liquids often occurs simultaneously with crystallization, making it difficult to separate the two phenomena13. A liquid–liquid critical point (LLCP), similar to the gas–liquid critical point, has been predicted at the end of the LLT line that separates the low- and high-density liquids in some cases, but has not yet been experimentally observed for any materials. This putative LLCP has been invoked to explain the thermodynamic anomalies of water1. Here we report combined in situ density, X-ray diffraction and Raman scattering measurements that provide direct evidence for a first-order LLT and an LLCP in sulfur. The transformation manifests itself as a sharp density jump between the low- and high-density liquids and by distinct features in the pair distribution function. We observe a non-monotonic variation of the density jump with increasing temperature: it first increases and then decreases when moving away from the critical point. This behaviour is linked to the competing effects of density and entropy in driving the transition. The existence of a first-order LLT and a critical point in sulfur could provide insight into the anomalous behaviour of important liquids such as water.

Folded network and structural transition in molten tin | Nature Communications volume 13, Article number: 126 (2022)
The fundamental relationships between the structure and properties of liquids are far from being well understood. For instance, the structural origins of many liquid anomalies still remain unclear, but liquid-liquid transitions (LLT) are believed to hold a key. However, experimental demonstrations of LLTs have been rather challenging. Here, we report experimental and theoretical evidence of a second-order-like LLT in molten tin, one which favors a percolating covalent bond network at high temperatures. The observed structural transition originates from the fluctuating metallic/covalent behavior of atomic bonding, and consequently a new paradigm of liquid structure emerges. The liquid structure, described in the form of a folded network, bridges two well-established structural models for disordered systems, i.e., the random packing of hard-spheres and a continuous random network, offering a large structural midground for liquids and glasses. Our findings provide an unparalleled physical picture of the atomic arrangement for a plethora of liquids, shedding light on the thermodynamic and dynamic anomalies of liquids but also entailing far-reaching implications for studying liquid polyamorphism and dynamical transitions in liquids.

Low viscosity of the Earth’s inner core | Nature Communications volume 10, Article number: 2483 (2019)
The Earth’s solid inner core is a highly attenuating medium. It consists mainly of iron. The high attenuation of sound wave propagation in the inner core is at odds with the widely accepted paradigm of hexagonal close-packed phase stability under inner core conditions, because sound waves propagate through the hexagonal iron without energy dissipation. Here we show by first-principles molecular dynamics that the body-centered cubic phase of iron, recently demonstrated to be thermodynamically stable under the inner core conditions, is considerably less elastic than the hexagonal phase. Being a crystalline phase, the body-centered cubic phase of iron possesses the viscosity close to that of a liquid iron. The high attenuation of sound in the inner core is due to the unique diffusion characteristic of the body-centered cubic phase. The low viscosity of iron in the inner core enables the convection and resolves a number of controversies.

Computational methods to simulate molten salt thermophysical properties | Communications Chemistry volume 5, Article number: 69 (2022)
Increased computational power and theoretical insights advanced inter-atomic potentials and density functional theory to the level of usefulness and reliability. Both fields were developed by different research groups whose insights influenced each other. Here, we will first review the progression of DFT-based methods, resulting in dispersion corrected ab-initio molecular dynamics (AIMD) methods. Afterward, we will discuss the advancements of inter-atomic potentials, as they were strongly influenced by the increase in accuracy and feasibility of DFT models.
In 1993, Barnnet and Lanmarn introduced Born-Oppenheimer molecular dynamics with DFT which enabled larger simulation timesteps resulting in a better temporal sampling of still very restricted simulations23. Later that year, Kresse and Hafner introduced the method of initializing Car-Parrinello Molecular Dynamics with energy minimization schemes for metals, resulting in additional speed up24. In 1998, Alfe extracted the diffusion coefficients using CPMD for liquid aluminum and thereby demonstrated the feasibility of DFT methods to extract transport properties in condensed materials25. In 2003, Aguado et al. developed an ab-initio process using many condensed phases of MgO with CATSTEP DFT to parametrize coefficients for the aspherical ion model (AIM) representing a breakthrough for polarizable models26. In 2005, Hazebroucq used a tight-binding density functional to calculate and investigate diffusion in NaCl and KCl for a specific experimental volume, a step towards full AIMD27.
In 2006 Madden et al. highlighted the need to control the dispersion interaction as it—despite being only a tiny fraction of the interaction energies of an ion pair—strongly impacted phase transition behavior, such as transition pressures28. It had been a well-known problem up to this time that dispersion interactions in DFT calculations were not accurate. Grimme tackled the problem of dispersion and published a first empirical correction in 2006 for DFT followed by a second in 2010, practically resolving the issue29. In 2006, Klix investigated the diffusion of tritium in Flibe, using CPMD, and named the method ab-initio molecular dynamics30. This simulation was the first time that a molten salt had dynamical behavior derived using ab-intio methods.

Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation | Nature Computational Science volume 2, pages367–377 (2022)
The marriage of density functional theory (DFT) and deep-learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent the DFT Hamiltonian (DeepH) of crystalline materials, aiming to bypass the computationally demanding self-consistent field iterations of DFT and substantially improve the efficiency of ab initio electronic-structure calculations. A general framework is proposed to deal with the large dimensionality and gauge (or rotation) covariance of the DFT Hamiltonian matrix by virtue of locality, and this is realized by a message-passing neural network for deep learning. High accuracy, high efficiency and good transferability of the DeepH method are generally demonstrated for various kinds of material system and physical property. The method provides a solution to the accuracy–efficiency dilemma of DFT and opens opportunities to explore large-scale material systems, as evidenced by a promising application in the study of twisted van der Waals materials.

Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures | Nature Communications volume 12, Article number: 7012 (2021)
The prediction of temperature effects from first principles is computationally demanding and typically too approximate for the engineering of high-temperature processes. Here, we introduce a hybrid approach combining zero-Kelvin first-principles calculations with a Gaussian process regression model trained on temperature-dependent reaction free energies. We apply this physics-based machine-learning model to the prediction of metal oxide reduction temperatures in high-temperature smelting processes that are commonly used for the extraction of metals from their ores and from electronics waste and have a significant impact on the global energy economy and greenhouse gas emissions. The hybrid model predicts accurate reduction temperatures of unseen oxides, is computationally efficient, and surpasses in accuracy computationally much more demanding first-principles simulations that explicitly include temperature effects. The approach provides a general paradigm for capturing the temperature dependence of reaction free energies and derived thermodynamic properties when limited experimental reference data is available.

Ab initio mechanism revealing for tricalcium silicate dissolution | Nature Communications volume 13, 1253 (2022)
Dissolution of minerals in water is ubiquitous in nature and industry, especially for the calcium silicate species. However, the behavior of such a complex chemical reaction is still unclear at atomic level. Here, we show that the ab initio molecular dynamics and metadynamics simulations enable quantitative analyses of reaction pathways, thermodynamics and kinetics of the calcium ion dissolution from the tricalcium silicate (Ca3SiO5) surface. The calcium sites with different coordination environments lead to different reaction pathways and free energy barriers. The low free energy barriers result in that the detachment of the calcium ion is a ligand exchange and auto-catalytic process. Moreover, the water adsorption, proton exchange and diffusion of water into the surface layer accelerate the leaching of the calcium ion from the surface step by step. The discovery in this work thus would be a landmark for revealing the mechanism of tricalcium silicate hydration.

The top 100 papers | Nature 514, 550–553 (2014).
When theorists want to model a piece of matter — be it a drug molecule or a slab of metal — they often use software to calculate the behaviour of the material’s electrons. From this knowledge flows an understanding of numerous other properties: a protein’s reactivity, for instance, or how easily Earth’s liquid iron outer core conducts heat.
Most of this software is built on density functional theory (DFT), easily the most heavily cited concept in the physical sciences. Twelve papers on the top-100 list relate to it, including 2 of the top 10. At its heart, DFT is an approximation that makes impossible mathematics easy, says Feliciano Giustino, a materials physicist at the University of Oxford, UK. To study electronic behaviour in a silicon crystal by taking account of how every electron and every nucleus interacts with every other electron and nucleus, a researcher would need to analyse one sextillion (1021) terabytes of data, he says — far beyond the capacity of any conceivable computer. DFT reduces the data requirement to just a few hundred kilobytes, well within the capacity of a standard laptop.
Theoretical physicist Walter Kohn led the development of DFT half a century ago in papers20, 21 that now rank as numbers 34 and 39. Kohn realized that he could calculate a system’s properties, such as its lowest energy state, by assuming that each electron reacts to all the others not as individuals, but as a smeared-out average. In principle, the mathematics are straightforward: the system behaves like a continuous fluid with a density that varies from point to point. Hence the theory’s name.
But a few decades passed before researchers found ways to implement the idea for real materials, says Giustino. Two22, 23 top-100 papers are technical recipes on which the most popular DFT methods and software packages are built. One (number 8) is by Axel Becke, a theoretical chemist at Dalhousie University in Halifax, Canada, and the other (number 7) is by US-based theoretical chemists Chengteh Lee, Weitao Yang and Robert Parr. In 1992, computational chemist John Pople (who would share the 1998 Nobel prize with Kohn) included a form of DFT in his popular Gaussian software package.
Software users probably cite the original theoretical papers even if they do not fully understand the theory, says Becke. “The theory, mathematics and computer software are specialized and are the concern of quantum physicists and chemists,” he says. “But the applications are endless. At a fundamental level, DFT can be used to describe all of chemistry, biochemistry, biology, nanosystems and materials. Everything in our terrestrial world depends on the motions of electrons — therefore, DFT literally underlies everything.”

Electronic-structure methods for materials design | Nature Materials, volume 20, pages736–749 (2021)
Applications of electronic-structure methods range from nanotechnology to planetary science, from metallurgy to quantum materials, and the current push and excitement to accelerate or complement experiments with simulations makes it even more urgent to highlight not only their capabilities but also their limitations. Simulations do not fail in spectacular ways, but can subtly shift from being invaluable to barely good enough to just useless. The reasons for failure are manifold, from stretching the capabilities of the methods to forsaking the complexity of real materials. But simulations are also irreplaceable: they can assess materials at conditions of pressure and temperature so extreme that no experiment on Earth is able to replicate, they can explore with ever-increasing nimbleness the vast space of materials phases and compositions in the search for that elusive materials breakthrough, and they can directly identify the microscopic causes and origin of a macroscopic property. Last, they share with all branches of computational science a key element of research: they can be made reproducible and open and shareable in ways that no physical infrastructure will ever be.

Ab initio molecular dynamics: Concepts, recent developments, and future trends | PNAS May 3, 2005
AIMD is a rapidly evolving and growing technique that constitutes one of the most important theoretical tools developed in the last decades. In an AIMD calculation, finite-temperature dynamical trajectories are generated by using forces obtained directly from electronic structure calculations performed “on the fly” as the simulation proceeds. Thus, AIMD permits chemical bond breaking and forming events to occur and accounts for electronic polarization effects (3, 4). AIMD has been successfully applied to a wide variety of important problems in physics and chemistry and is now beginning to influence biology as well. In numerous studies, new physical phenomena have been revealed and microscopic mechanisms elucidated that could not have been uncovered by using empirical methods, often leading to new interpretations of experimental data and even suggesting new experiments to perform.

Ab Initio Molecular Dynamics Simulations | J. Phys. Chem. 1996
Classical molecular dynamics (MD), which has its origins in the 1950s and 1960s, is now widely used to investigate condensed phase systems, ranging from micellar solutions to biomolecules, polymers, and inorganic materials. The standard MD technique, which is based on the use of empirical interatomic potential functions parametrized to experimental data, has also proved its worth in the study of ionic solutions, electrochemistry, tribology, and cluster science. One of the most exciting developments of the past decade is the advent of ab initio molecular dynamics, which, rather than requiring an empirical interaction potential as input, utilizes interatomic forces computed directly from the electronic structure. This new approach has now become established as an important tool in the investigation of chemically complex environments. In the Car−Parrinello (CP) approach 1,2 to ab initio MD, the electronic structure is described using the Kohn−Sham formulation 3 of the density functional theory, 4 and the Kohn−Sham orbitals are expanded in a plane wave basis. The expansion coefficients are treated as a set of fictitious dynamical variables that are propagated adiabatically with respect to the nuclei, so that, at each time step, they describe the instantaneous ground state Born−Oppenheimer surface. In this way, the need to solve the Kohn−Sham equations explicitly is avoided. The CP approach has proved useful in a wide variety of physical and chemical applications. The present article illustrates the versatility of the CP approach to MD simulations by considering a variety of condensed phase systems.

Discovering and understanding materials through computation | Nature Materials volume 20, pages728–735 (2021)
Parallel to developments in computational electronic structure, first-principles methods were advanced to explore atomic motions. As electrons are much lighter than nuclei, to a high degree of accuracy, the electrons may be assumed to follow the nuclei instantly in the Born–Oppenheimer adiabatic approximation. However, solving for the electronic structures at each nuclear configuration remains a complicated problem even with the advent of DFT, as it introduces many new degrees of freedom owing to the motion of the nuclei. Moreover, the coupling of electrons to the nuclei going beyond the Born–Oppenheimer approximation is central to many important processes.
Over the past half century, DFT has been arguably the most dominant and successful method for the ab initio computational study of materials’ ground-state properties. The foundations of DFT were laid down in the work of Hohenberg and Kohn6, which showed that the electronic ground-state total energy can be expressed as a functional of the ground-state charge density, and of Kohn and Sham7, which showed that the ground-state charge density can be determined by a set of self-consistent one-body equations. Within Kohn–Sham DFT, the difficult problem of many interacting electrons is mapped to a one-electron Schrödinger equation of a fictitious system of non-interacting particles with the same density. In principle, DFT is an exact theory for ground-state properties—such as molecular and crystal structure, elastic constants and vibrational properties, and the relative stability of different structural phases, among others2. In practice, an approximation for the effective one-particle exchange-correlation potential (a term from the interacting electrons) is needed. Earlier works approximate the exchange-correlation potential as a function of the local charge density in the local density approximation7,8,9. Later developments introduce additional features (from theory or empiricism) based on the gradient of the density or inclusion of a Hartree–Fock-like exchange term. The development of more accurate and universal density functionals is an active research direction. These advances, combined with the implementation of increasingly sophisticated DFT computer codes and algorithms through the years, have tremendously broadened the scope of physical systems that can be studied10. Moreover, DFT results are frequently used as input for the parameterization of coarse-grained models for extended spatial and timescale phenomena, opening the doorway for multiscale modelling rooted in ab initio theory, as discussed in the four Review Articles2,3,4,5.

Self-consistent determination of long-range electrostatics in neural network potentials | Nature Communications volume 13, 1572 (2022)
Computer simulations have transformed our understanding of molecular systems by providing atomic-level insights into phenomena of widespread importance. The earliest models used efficient empirical descriptions of interatomic interactions, and similar force field-based simulations form the foundation of molecular simulations today1. However, it is difficult to describe processes like chemical reactions that involve bond breakage and formation, as well as electronic polarization effects within empirical force fields. The development of quantum mechanics-based ab initio simulations enabled the description of these complex processes, leading to profound insights across scientific disciplines2,3,4,5,6,7,8,9. The vast majority of these first principles approaches rely on density functional theory (DFT), and the development of increasingly accurate density functionals has greatly improved the reliability of ab initio predictions10,11,12,13,14,15. But, performing electronic structure calculations are expensive, and first-principles simulations are limited to small system sizes and short time scales.
The prohibitive expense of ab initio simulations can be overcome through machine learning. Armed with a set of ab initio data, machine learning can be used to train neural network (NN) potentials that describe interatomic interactions at the same level of accuracy as the ab initio methods, but with a fraction of the cost. Consequently, NN potentials enable ab initio quality simulations to reach the large system sizes and long time scales needed to model complex phenomena, such as phase diagrams16,17,18,19,20 and nucleation21,22.

cp2k: atomistic simulations of condensed matter systems
Computer simulation of matter with atomistic detail has become a very prominent tool in chemistry, physics, life sciences, and materials sciences. In these fields, simulation results can yield the insights needed to interpret experimental measurements, can be used to predict material properties, or to design new compounds. A precise picture of the structures and dynamical processes at the atomic scale is a valuable starting point to rationally design new experiments and new systems. With sustained exponential growth in computer resources, the impact of simulation will continue to increase.
CP2K has a large impact in the field of density functional theory (DFT)-based molecular dynamics (MD) simulation, particularly with its capability to describe the dynamics of systems containing hundreds of atoms with relative ease, but has a broader range of capabilities.
To study processes dominated by rare events,like reactions and structural transformations, MD is typically not sufficient due to too slow sampling of the configurations space. Constraints and restraints can be employed to reconstruct the free energy profile along specific reaction pathways by applying methods like thermodynamic integration or umbrella sampling.8 Metadynamics is another powerful method to accelerate the sampling and reconstruct the free energy surfaces in terms of a few collective variables.24,25 In CP2K, different versions of metadynamics are implemented, such as extended Lagrangian metadynamics, well-tempered metadynamics, and the multiwalker scheme.25

Ab initio simulation: The correlation between the local melt structure and segregation behavior of Fe, V, Ti and Si in liquid Al
The rapid development of computer technology has allowed researchers to study melt structures and their solid–liquid interface with atomic level simulations. In particular, simulations based on ab initio molecular dynamics (AIMD) are widely used because of the higher accuracy of AIMD compared to other computational methods. The AIMD simulation method has been proven to be a powerful technique for simulating the properties of molten metal [5], [6], [7], [8].This is especially true for Al and Al alloy melts, which have relatively simple crystalline structures; thus, extensive and deep insights into these systems have been gained [9], [10], [11].

The migration behavior of the fourth period transition metals in liquid Al: An ab initio molecular dynamics study
In this study, for extending previous work, we investigated the local structure characters around all FPTMs solute atoms in the Al melt and researched the correlation between the variation of the diffusivity and changing of local structure of the FPTMs solute atoms, then tried to explain the influence law of diffusivity affected by local structure. However, the challenges of directly detecting molten structure have been beyond the capabilities of experimentalists. Characterization of the local structures around a certain atom in the melt is harder than one can imagine. Ab initio molecular dynamics (AIMD) make researching of the metallic melt at atomic level become possible. In previous work, it was proved that AIMD is powerful to research the melt characters [16], [17], [18], [19], [20], [21], [22], [23] and widely accepted.

Melts of CrCoNi-based high-entropy alloys: Atomic diffusion and electronic/atomic structure from ab initio simulation
Despite the importance of liquid-state properties, their experimental measurement in high-temperature melts is challenging, and ab initio molecular-dynamics (AIMD) simulations have emerged as a powerful framework for their calculation.

An ab initio molecular dynamics exploration of associates in Ba-Bi liquid with strong ordering trends
In a recent CALPHAD (calculation of phase diagram) modeling [24] of the Ba-Bi system, Liu et al. [10] used two fictive associates of Ba4Bi3 and BaBi3 in the liquid phase, which were empirically selected based on the intermetallic compounds with the two highest congruent melting points. The present investigation aims to use the ab initio molecular dynamics (AIMD) simulations to study the presence and the characters of associates in the Ba-Bi liquid phase. The AIMD approach [25] uses the interatomic interactions calculated on the fly based on density functional theory (DFT), avoiding the errors due to empirical potentials.

Quickstep: Fast and accurate density functional calculations using a mixed Gaussian and plane waves approach
Density functional theory [1], [2] (DFT) is a well established method to perform electronic structure calculations. The accuracy of the method is such that many properties of systems of interest to chemistry, physics, material science, and biology can be predicted in a parameter free way. The standard computational approach to DFT is already efficient and thus appropriate for fairly large systems, currently about 100 atoms. Nevertheless, the computation of the Hartree (Coulomb) energy and the orthogonalisation of the wave functions are not scaling linearly with system size, and these terms therefore dominate the computational cost for larger systems [3]. The hybrid Gaussian and plane waves (GPW) method [4] provides an efficient way to treat these terms accurately at a significantly reduced cost. We present here the implementation of this method in Quickstep, which is part of the freely available program package CP2K [5].

A hybrid Gaussian and plane wave density functional scheme
Calculations based on density functional theory (DFT) [1] have enjoyed great success in recent years, with applications that range from materials science to chemistry and biochemistry. While conventional quantum chemical methods are amply used, an increasing number of applications make use of pseudo- potentials and the expansion of the Kohn±Sham (KS) [2] orbitals in plane waves (PWs), especially in the context of ab initio molecular dynamics simulations [3]. PW as a basis set for quantum chemical problems is a rather unusual and unnatural choice. However, PWs have a series of advantages that are at the heart of their success. PWs are atomic position independent; this makes the calculation of the Hellmann±Feynman forces very simple. PWs are totally unbiased and do not lead to basis set superposition errors. The calculation of the Hartree potential is very simple and checking the con- vergence of the calculation is trivial. Furthermore, the use of the fast Fourier transform technique considerably simplies many algebraic manipulations. There are, however, signi®cant disadvantages in using PWs. Most noticeably, a large number of PWs is needed to repro- duce the rapid variations of the wavefunctions close to the nuclei. This is alleviated by the use of pseudo- potentials, but for several elements unreasonably large basis sets are still needed.

Ab Initio Molecular Dynamics Study of 45S5 Bioactive Silicate Glass
Classical molecular dynamics (MD) simulations are well suited to model simple silicate and phosphate glasses; 12-17 however, multicomponent phosphosilicate glasses may represent a challenge for classical MD, due to the difficulty of incorporating many different interactions and complex effects in a reliable force field. An alternative computational approach, unhindered by the need to develop an accurate potential model due to its first-principle calculation of energy and ionic forces, is represented by ab initio molecular dynamics (AIMD), such as the Car−Parrinello method. 18,19 This approach has been successfully applied to investigate structural and electronic effects in silica and modified silicate glasses. 20-24 The large computational resources needed by the explicit inclusion of electronic degrees of freedom limit the properties which can be effectively explored in AIMD simulations of glasses:  structural features beyond local order, such as network connectivity or the Qn distribution, as well as long-range ionic migration pathways, lie outside the space/time ranges accessible with AIMD. On the other hand, the ionic vibrational motion in condensed phases can be adequately sampled in AIMD simulations, and they have been successfully employed to determine vibrational and elastic properties of silicate glasses.22,25,26 In addition, AIMD simulations give straightforward access to the electronic properties of glasses, 27,28 which are essential to complete the picture of the bulk properties of these materials related to their bioactivity.

First principle study of electronic structural and physical properties of CaO-SiO2-Al2O3 ternary slag system by using Ab Initio molecular and lattice dynamics
As most of the properties on melts would be explained through understanding the behavior of electrons, which can hold atoms together by making bonds. The electronic structure theory and ab initio electronic structure calculations can predict the physical and chemical properties as well as structure of slag [8]. To understand the electronic properties the density of state (DOS), electron density difference (EDD), atomic and bond population are calculated for CSA slag system by using ab initio lattice dynamics (AILD), first principle simulation technique. The modern computational technology has made ab initio molecular dynamics (AIMD) accessible, and emerging simulation method [9] to calculate the physical properties of slags. The Classical molecular dynamics (CMD) simulations based on empirical interatomic potentials, are inexpensive in computer time and simulate the large systems with number of atoms, (N > 1000) but the quality of the results depends on the accuracy of the input force field. While the ab initio DFT simulations which uses the electron’s density for the calculation of properties, gives the more precise results. However, it is expensive in computer time and restricted to small systems (N ~ 100 atoms) [10].

Comparison of desulfurization mechanism in liquid CaO-SiO2 and MnO-SiO2: An ab initio molecular dynamics simulation
With the rapid progress of computer science in recent years, a considerable number of simulation studies on silicate structures have been conducted [19], [20], [21], [22], [23], [24]. For example, in silicate systems, classical molecular dynamics (MD) is frequently utilized to provide structural and coordination information [23], [25], [26], [27], [28], [29]. However, it can only explain molecular interactions in the ground state and not information about atom interactions, such as charge distribution. Ab Initio molecular dynamics (AIMD) based on quantum mechanics theory, which can direct calculation of all molecular and molecular interactions through quantum chemistry methods, no need to input empirical mechanical models. It makes up for the shortcomings of classical molecular dynamics simulation and can have a more essential understanding of the melting structure and charge information of silicate under high-temperature conditions. This method is widely used in materials and chemistry [30], [31], [32] and can deepen understanding of the existing state mechanism of trace elements. At the same time, AIMD simulation has mature applications for the melting process of liquid metal and oxide systems [20], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44]. For the silicate oxide system, Koler [38] and Georg [45] studied thermodynamic properties and structural information of MgO-SiO2 by AIMD. Huang [46] investigated the Fe-Si-O system from 3800 K to 4800 K under high pressure. For CaO-SiO2 and MnO-SiO2 systems, researchers [47] have used DFT-MD calculations to study the structural properties of CaO-SiO2 with different proportions.

Modelling the local atomic structure of molybdenum in nuclear waste glasses with ab initio molecular dynamics simulations
Molecular dynamics (MD) is a computational method of choice for efficiently probing the various heterogeneous local environments found in glasses, as it provides insight into the material properties from the atomistic level.13 However, classical MD simulations are hampered by the lack of reliable force fields to describe the complex interactions in multicomponent borosilicate glasses,14 even though reliable interatomic potentials have been developed to model pure silica and mixed cation silicate glasses.15 An alternative computational approach is the ab initio molecular dynamics technique (AIMD), a parameter free approach, where the forces are computed from a quantum mechanical representation of the electronic structure. Despite being computationally demanding, as compared to classical MD simulations, this approach enables accurate modelling of many-body systems, and it can account for switching chemical bonds and electron density polarisation. AIMD simulations have previously been used to model the structural properties of pure silica and alkali silicate glasses,16–20 as well as, sodium borosilicate glass21 and bioactive phosphate glasses.22–25

Molecular Dynamics Modeling of the Structure and Na+-Ion Transport in Na2S + SiS2 Glassy Electrolytes
Ab initio molecular dynamics (MD), which performs quantum calculations to determine the forces on atoms and solves classical equations of motion at specific temperatures, is therefore well suited to analyze ion migration within these glasses and relate them to the local structure. Furthermore, ab initio MD can capture complex chemical reactions during the formation of glasses by melt-quench processes and can generate representative structures, as evidenced from previous work of Islam et al. (34−36) and Ceder et al. (37−39) on lithium oxide- and lithium sulfide-based glasses. Shah et al. summarized different multiscale modeling techniques addressing the complex issues for developing high-performance lithium-ion batteries. (40)

Coordination of Zr4+/Hf4+/Nb5+/Ta5+ in silicate melts: insight from first principles molecular dynamics simulations
Numerous FPMD simulations of various melts including oxides (e.g., Karki et al., 2007; Verma et al., 2011), silicates (e.g., Karki and Stixrude, 2010a; Ni and de Koker, 2011; Sun et al., 2019) and carbonates (e.g., Vuilleumier et al., 2014; Zhang and Liu, 2015) have been reported. These studies focused on the physical properties of melts, but less attention has been paid to the geochemical behavior of ore-forming metals in melts (e.g., Wagner et al., 2017, Wagner et al., 2017b). To the best of our knowledge, the coordination chemistry of Zr, Hf, Nb and Ta in silicate melts has not been investigated by using FPMD.

Ab initio molecular dynamics assessment of thermodynamic and transport properties in (K,Li)Cl and (K, Na)Cl molten salt mixtures
However, measuring properties of MSs at very high temperatures under hazardous or corrosive conditions is difficult and costly. In addition, MSs are liquid-phase multi-component mixtures whose local structure and speciation are very difficult to determine. Luckily, computer simulations using theoretical modeling techniques such as molecular dynamics or Monte Carlo provide a low-cost alternative approach [[8], [9], [10]]. For atomistic simulations of MS systems, the treatment of inter-atomic interactions is crucial. Since the early days of computer-based molecular simulations, MS modeling has been largely done using force field potentials [8,9,11]. Most commonly, the inter-atomic forces are described with additive pairwise potentials consisting of terms like Coulombic attraction/repulsion, van der Waals dipole-dipole/dipole-quadrupole interactions [12,13]. These so-called rigid ion models omit inter-atomic forces arising from polarization. Rahman et al. showed that on top of these two-body potentials, the polarization, treated in a shell-model type fashion [14], leads to a significant increase in ionic diffusion coefficient, and in a reduction in the characteristic frequencies [15]. The parameterization of these potentials largely depends on reproducing experimental data. More recently, sophisticated procedures based on first principles for potential parameterization assessing polarization have been applied and showed certain success in reproducing static and dynamic properties of MSs system [9,[16], [17], [18]]. However, such a procedure based on the fitting with first principles potentials becomes challenging as the time scale and the complexity of multicomponent systems increase. One can always question the transferability of these potentials as physical and chemical properties of an ion can change significantly from on coordination environment to another. Furthermore, classical force fields implicitly ignore the electronic degrees of freedom when ions change their coordination environments. As such, processes including charge transfer are not captured, and the most obvious consequence is that important chemical processes such as redox cannot be described.
Recently quantum mechanics-based molecular dynamics has been applied to study MSs [[19], [20], [21], [22], [23], [24], [25]]. Galamba et al. [19,20]., by employing density functional theory (DFT) – based molecular dynamics (MD) simulations to study molten NaCl and NaI, found that the main differences in structural and transport properties between results from the rigid ion and ab initio approaches are related to polarization effects. Bengtson et al. [22], performing ab initio molecular dynamics (AIMD) simulations on the eutectic (K, Li)Cl mixture, showed that AIMD is a useful predictive tool for properties such as bulk modulus of which experimental data are not available, or complex thermodynamics properties like the free energy of mixing which help understand phase stability.

Properties of Negatively Charged Ruthenium Clusters in Molten Sodium Chloride
In this study, we use ab initio molecular dynamics (AIMD) to investigate the stability of negatively charged ruthenium clusters in molten sodium chloride and their ability to stabilize negatively charged adsorbates/intermediates. The study builds on a recent study from our group, where the reactivity of solvated electrons was investigated in molten alkali chloride.

Alternative insight into aluminium-phosphate glass network from ab initio molecular dynamics simulations
Depending on the method of description of the interatomic interactions it can be divided on classical or ab initio [26,27,29,[37], [38], [39], [40], [41], [42]]. The classical molecular dynamics (MD) use interatomic potentials based on empirical data or electronic structure calculations to determine the forces between ions [43,44]. The big advantage of the classical MD is the ability to simulate large systems (1 000 000 ions) at current computer performance [41,45]. The main problem of the classical molecular dynamics is matching the potential so that it accurately describes the interaction between ions. This problem does not occur in ab initio MD. This method relies on calculating the forces acting on the nuclei from electron structure calculations. The calculations of electron structure are performed during generating the trajectory of nuclei. One of the ab initio MD methods is Car-Parrinello molecular dynamics (CPMD). This method was used to study the structures of phosphate glasses and gave more accurate results than classical MD [29,[46], [47], [48]]. The ab initio methods are very computationally demanding and are limited to only hundreds of atoms.

Java2022
2022 入坑 Java ?你要知道的十万个为什么…全套 Java 教程送给你,限时领取哦…
2022 年 Java 开发:预测和选定趋势
2022 年 Java 将何去何从?
Java 2022 生态报告: Java 11 超越 Java 8
2022 年 Java 未来的 5 种技术趋势
微软公布 VS Code Java 2022 年路线图!
你还在用 Java 8?《 2022年 Java 开发者生产力报告》来了!
Java 核心技术大会 2022 · 重磅发布
2022年 Java 开发报告出炉, Java 8 屹立八年不倒!
微软公布VS Code Java 2022年 路线图
2022年 Java 行业分析报告,全面解析别错过
2022 年 Java 开发者都在用什么?
2022年 , Java 开发的“钱途”如何?
2022年 学习 JAVA 怎么样?
2022年 ,劝搞 Java 的千万不要跳槽!
2022年 Java 工程师就业前景分析!
2022 保姆级 Java 免费教程,一次性给你找全了!
C++ 即将超越 Java , 2022年 6月编程语言排行榜发布!
尚硅谷 2022 版 Java 课程体系,霸气来袭!
VS Code Java 2022年 路线图
2022 全球程序员报告出炉! Java 居然连续七年无缘“最受欢迎语言”?
Java 之父呼吁大家弃用 Java 8? 2022 年 Java 开发者都在用什么
2022年 学 Java 还有前景吗?我们用数据说话!
2022 年 Java 将何去何从?(文末送书)
2022年 了,现在学习 Java 还合适吗?
【周三热点】《 2022年 Java 开发者生产力报告》出炉!
2022 年面向开发人员的 7 个最佳 Java IDE
《 2022 年 Java 开发者生产力报告》
2022年 选择 Java ,会输吗?
2022 版首发,阿里 Java 开发手册(黄山版).PDF
2022年 9 个最佳 Java 测试框架
Java 8不倒、IntelliJ IDEA力压Eclipse, 2022年 Java 开发者在用什么?
Java 核心技术大会 2022 l 圆满闭幕
【lol学习路线图】| 2022 最新 Java 学习路线图-第一集 北地禁魔
大计动态| 2022 JAVA 知识抢答赛圆满结束
2022年 下半年开始学 Java ,晚吗?
2022年 ,初级 Java 开发人员应该掌握哪些框架?
2022 大厂校招,企业对 Java 有多看重?
【lol学习路线图】| 2022 最新 Java 学习路线图-第七集 失落之地的辉煌(完结篇)
前端JS霸榜, Java 稳居第三 | 2022年 Q1编程语言排行榜出炉
入职大厂,月薪20K?! 2022 最新 Java 技能学习路线图你不来看看吗?
【lol学习路线图】| 2022 最新 Java 学习路线图-第五集 初生之地的崛起
2022 第11期全新一代 Java 进阶+云原生架构班课程表
【lol学习路线图】| 2022 最新 Java 学习路线图-第二集 帝国的野望
【lol学习路线图】| 2022 最新 Java 学习路线图-第四集 丛林沙漠巨神峰
【lol学习路线图】| 2022 最新 Java 学习路线图-第三集 双城的生意经
关于 2022年 1+X Java Web应用开发职业技能等级证书考核计划安排的通知
【lol学习路线图】| 2022 最新 Java 学习路线图-第六集 神秘之地的筹谋
2022 版新鲜出炉,246页 Java 高频面试真题及详解汇总 | 极客时间
2022年 , 薪酬最高的5种编程语言, Java 、SQL靠边站, 第一竟是…
2022年 王道训练营 Java 工程师方向课程大纲
直播预告| 2022 中软国际“ JAVA 应用开发”、“数据应用开发与服务(Python)”1+X证书试点工作宣讲会
Java 学科 2022 0525班,平均薪资9369.12元,最高薪资19800元毕业61个工作日,就业率91.89%
2022 技术趋势预:Python、 Java 占主导,Rust、Go增长迅速,元宇宙成为关注焦点测
“银”火计划 | 金融科技部 2022年 Java 实战营开营啦!
【国企】 Java 开发、前端共4人, 2022年 云南出版集团所属云南出版融媒体有限公司招聘公告——头职
贵州智慧农业云团队 2022年 招聘 JAVA 开发工作人员3人
邀请函 | 2022年 东软1+X Java Web应用开发职业技能等级证书试点工作说明会
2022 秋招大战:算法岗挤破头, JAVA 开发也被迫内卷
2022 届 | 蚂蚁金服 java 暑期实习生面经
2022 届 | 阿里云技术团队3轮 java 面经分享
2022年 技术趋势预测:Python、 Java 占主导,元宇宙持续受关注!
Python、 Java 占主导,Rust、Go增长迅速,元宇宙成为关注焦点| 2022 技术趋势预测
2022年 软考中高级视频+真题 + Java 《八股文》PDF手册免费领取
Java 近期新闻:Payara Platform 2022 路线图、OpenJDK JEP 草案、Gradle 7.4
Python/ Java 开发工程师 |时薪400大学生线上兼职家教|网联清算2021届应届生及 2022 届实习生招聘简章
Oracle Java SE 输入验证错误漏洞通告(CVE- 2022 -21449)POC已公布
Java SE 数字签名伪造漏洞通告(CVE- 2022 -21449)
【漏洞预警】Oracle Java SE输入验证错误漏洞(CVE- 2022 -21449)
百战程序员 Java 金牌课程,创“薪”未来,最新课程更新 2022 .7.22
JAVA 开发工程师、产品经理| 2022年 1月25日发布招聘信息
校园招聘 | 【20k+ Java /算法】品览 2022 届春招
【佛山招聘】6500-7499元/月 JAVA 工程师- 2022 届春招
广州校招 | SHEIN全职招聘 JAVA 工程师(广州)- 2022 届
Java 8“失宠”
2022年 直播课 | 轻松入门基于Spring的 Java Web开发
Java 之父呼吁大家弃用 Java 8
又一编程排行榜出炉,Python、 Java 都不是第一!
2022年 最新 Java 程序员面试宝典 免费领取
2022 技术趋势报告:C++ 重新“受宠”| “data”、“Python”、“ Java ”上榜热搜词
InfoQ 最新 Java 发展趋势报告
HTML5崛起之时, Java 桌面时代就已经终结了
2022 年十大 JavaScript 框架
faker.js创建者希望GitHub恢复他的权利;微软公布VS Code 2022年 路线图; Java 18新特性 | 开源日报
测试 C、Python、 Java 等 16 种编程语言的 Hello World:7 种存在 Bug?
StackOverflow 2022 年度调查报告:JavaScript 连续霸榜, Java 被挤出前五,Rust 最受欢迎
Java 近期新闻:OpenJDK 更新、Spring Framework 6.0-M3、JobRunr 5.0-M1
Java 18 正式发布:默认字符集 UTF-8,附带简易 HTTP 服务器,弃用 Finalization
IDEA 2022 .2.1 Beta 2发布:新增支持 Java 18、增强JUnit 5的支持
Java 近期新闻:Loom 项目、Spring、Payara、Open Liberty 及 JReleaser 升级
Java 近期新闻:JDK 18 GA、JMC 8.2、Spring 升级、MicroStream 7.0-Beta1
Java 近期新闻:JDK 19 与 Jakarta EE 10 的更新以及 Amazon Corretto 的异步、缓冲日志
Java 近期新闻:Spring 项目更新、值对象(预览)JEP 以及 Quarkus 2.7.2
胜也 Java ,败也 Java ,请用心对待它!
InfoQ 2022 年趋势报告:架构与设计篇
《 2022 女程序员人群洞察报告》有哪些新看点?
2022 年,捕捉这 12 个数据和分析趋势!
2022年 ,选前端,拿高薪没压力!
2022 最新版Spring框架教程正式上线,效率与质量并存,课程与实战紧密相关,市面仅有,速度来免费领取吧!
高才计划 | Java2022 02期直航名企免费培训课程规划通知