skphy / MLinQCbook22

Mirror of the companion website for Quantum Chemistry in the Age of Machine Learning edited by Pavlo O. Dral

Quantum Chemistry in the Age of Machine Learning (paperback ISBN: 9780323900492) is a book edited by Pavlo O. Dral.

This website collects complimentary electronic material and links to repositories with programs, data, instructions, sample input and output files required for case studies as well as any post-publication updates.

Material for Case studies

Part 1. Introduction

Chapter 1. Very brief introduction to quantum chemistry by Xun Wu and Peifeng Su

https://github.com/dralgroup/MLinQCbook22-CH01

Chapter 2. Density functional theory by Hong Jiang and Huai-Yang Sun

https://github.com/ffshy/ChapterDFTCaseStudy

Chapter 3. Semiempirical quantum mechanical methods by Pavlo O. Dral and Jan Řezáč

https://github.com/dralgroup/MLinQCbook22-SQM

Chapter 4. From small molecules to solid-state materials: A brief discourse on an example of carbon compounds by Bili Chen, Leyuan Cui, Shuai Wang, Gang Fu

https://github.com/bili0501/MLinQCbook22-CH04

Chapter 5. Basics of dynamics by Xinxin Zhong and Yi Zhao

https://github.com/Cindy611/TDQD

Chapter 6. Machine learning: An overview by Eugen Hruska and Fang Liu

https://github.com/Liu-group/MLbook

Chapter 7. Unsupervised learning by Rose K. Cersonsky and Sandip De

https://github.com/rosecers/unsupervised-ml

Chapter 8. Neural networks by Pavlo O. Dral, Alexei Kananenka, Fuchun Ge, Bao-Xin Xue

https://github.com/dralgroup/MLinQCbook22-NN

Chapter 9. Kernel methods by Max Pinheiro Jr and Pavlo O. Dral

https://github.com/dralgroup/MLinQCbook22-NN

Chapter 10. Bayesian inference by Wei Liang and Hongsheng Dai

https://github.com/WeiLiangXMU/Bayesian-Inference

Part 2. Machine learning potentials

Chapter 11. Potentials based on linear models by Gauthier Tallec, Gaétan Laurens, Owen Fresse–Colson, Julien Lam

https://github.com/julienlamcnrs/Exercices-Potentials-based-on-linear-models.git

Chapter 12. Neural network potentials by Jinzhe Zeng, Liqun Cao, Tong Zhu

https://github.com/tongzhugroup/Chapter13-tutorial

Chapter 13. Kernel method potentials by Yi-Fan Hou and Pavlo O. Dral

https://github.com/dralgroup/MLinQCbook22-KMP

Chapter 14. Constructing machine learning potentials with active learning by Cheng Shang and Zhi-Pan Liu

www.lasphub.com/supportings/Li-GMsearch-AL.tgz

Chapter 15. Excited-state dynamics with machine learning by Lina Zhang, Arif Ullah, Max Pinheiro Jr, Mario Barbatti, Pavlo O. Dral

https://github.com/maxjr82/MLinQCbook16-NAMD

Chapter 16. Machine learning for vibrational spectroscopy by Sergei Manzhos, Manabu Ihara, Tucker Carrington

https://github.com/sergeimanzhos/QCAML

Chapter 17. Molecular structure optimizations with Gaussian process regression by Roland Lindh and Ignacio Fernández Galván

Download from the companion website: https://www.elsevier.com/__data/assets/file/0005/1295033/part2-chapter17files.zip

Part 3. Machine learning of quantum chemical properties

Chapter 18. Learning electron densities by Bruno Cuevas-Zuviría

https://github.com/brunocuevas/density-learning-tutorials

Chapter 19. Learning dipole moments and polarizabilities by Yaolong Zhang, Jun Jiang, Bin Jiang

https://github.com/zylustc/Learning-Dipole-Moments-and-Polarizabilities

Chapter 20. Learning excited-state properties by Julia Westermayr, Pavlo O. Dral, Philipp Marquetand

Case study 1

http://mlatom.com/mlinqcbook22-mlesprops/

Case study 2

Code and tutorial: https://github.com/schnarc/SchNarc/tree/DipoleMoments_Spectra Data: https://bit.ly/3lnUaZb

Part 4. Machine learning-improved quantum chemical methods

Chapter 21. Learning from multiple quantum chemical methods: Δ-learning, transfer learning, co-kriging, and beyond by Pavlo O. Dral, Tetiana Zubatiuk, Bao-Xin Xue

https://github.com/dralgroup/MLinQCbook22-delta

Chapter 22. Data-driven acceleration of coupled-cluster and perturbation theory methods by Grier M. Jones, P. D. Varuna S. Pathirage, Konstantinos D. Vogiatzis

Code examples of the case studies: https://ChemRacer.github.io/DDQC_Demo/ Source code: https://github.com/ChemRacer/DDQC_Demo

Chapter 23. Redesigning density functional theory with machine learning by Jiang Wu, Guanhua Chen, Jingchun Wang, Xiao Zheng

https://github.com/zhouyyc6782/oep-wy-xcnn

Chapter 24. Improving semiempirical quantum mechanical methods with machine learning by Pavlo O. Dral and Tetiana Zubatiuk

Initial guess for the ethylene geometry:

6

C       -0.723601672      0.000000000     -1.235611088
C       -0.723601672      0.000000000      0.094546912
H       -0.723601672      0.923341000     -1.808561088
H       -0.723601672     -0.923341000     -1.808561088
H       -0.723601672      0.923341000      0.667496912
H       -0.723601672     -0.923341000      0.667496912

Follow the instructions at http://mlatom.com/AIQM1 to perform geometry optimization and thermochemical calculations with AIQM1.

Chapter 25. Machine learning wavefunction by Stefano Battaglia

https://github.com/stefabat/MLWavefunction

Part 5. Analysis of Big Data

Chapter 26. Analysis of nonadiabatic molecular dynamics trajectories by Yifei Zhu, Jiawei Peng, Hong Liu and Zhenggang Lan

Case study 1

https://figshare.com/articles/dataset/Case_study_1_Classical_MDS_analysis_of_CH2NH2_dynamics/17110610

Case study 2

https://figshare.com/articles/dataset/Case_study_2_Fr_chet_distance_analysis_of_phytochromobilin/17104457

Case study 3

https://figshare.com/articles/dataset/Case_study_3_PCA_of_site-exciton_model_dynamics/17110592

Chapter 27. Design of organic materials with tailored optical properties: Predicting quantum-chemical polarizabilities and derived quantities by Gaurav Vishwakarma, Aditya Sonpal, Aatish Pradhan, Mojtaba Haghighatlari, Mohammad Atif Faiz Afzal, Johannes Hachmann

Code snippets are provided directly in the chapter text.