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Quantum Chemistry in the Age of Machine Learning

Book Companion

Quantum Chemistry in the Age of Machine Learning

Edition 1

Welcome to the Companion site for Quantum Chemistry in the Age of Machine Learning, 1st Edition

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 opens in new tab/window

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

https://github.com/ffshy/ChapterDFTCaseStudy opens in new tab/window

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

https://github.com/dralgroup/MLinQCbook22-SQM opens in new tab/window

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 opens in new tab/window

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

https://github.com/Cindy611/TDQD opens in new tab/window

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

https://github.com/Liu-group/MLbook opens in new tab/window

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

https://github.com/rosecers/unsupervised-ml opens in new tab/window

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

https://github.com/dralgroup/MLinQCbook22-NN opens in new tab/window

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

https://github.com/dralgroup/MLinQCbook22-NN opens in new tab/window

Chapter 10. Bayesian inference by Wei Liang and Hongsheng Dai

https://github.com/WeiLiangXMU/Bayesian-Inference opens in new tab/window

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 opens in new tab/window

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

https://github.com/tongzhugroup/Chapter13-tutorial opens in new tab/window

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

https://github.com/dralgroup/MLinQCbook22-KMP opens in new tab/window

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

www.lasphub.com/supportings/Li-GMsearch-AL.tgz opens in new tab/window

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 opens in new tab/window

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

https://github.com/sergeimanzhos/QCAML opens in new tab/window

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

Download opens in new tab/window

Part 3. Machine learning of quantum chemical properties

Chapter 18. Learning electron densities by Bruno Cuevas-Zuviría

https://github.com/brunocuevas/density-learning-tutorials opens in new tab/window

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

https://github.com/zylustc/Learning-Dipole-Moments-and-Polarizabilities opens in new tab/window

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

Case study 1

http://mlatom.com/mlinqcbook22-mlesprops/ opens in new tab/window

Case study 2

Code and tutorial: https://github.com/schnarc/SchNarc/tree/DipoleMoments_Spectra opens in new tab/window Data: https://bit.ly/3lnUaZb opens in new tab/window

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 opens in new tab/window

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/ opens in new tab/window Source code: https://github.com/ChemRacer/DDQC_Demo opens in new tab/window

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 opens in new tab/window

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 opens in new tab/window to perform geometry optimization and thermochemical calculations with AIQM1.

Chapter 25. Machine learning wavefunction by Stefano Battaglia

https://github.com/stefabat/MLWavefunction opens in new tab/window

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 opens in new tab/window

Case study 2

https://figshare.com/articles/dataset/Case_study_2_Fr_chet_distance_analysis_of_phytochromobilin/17104457 opens in new tab/window

Case study 3

https://figshare.com/articles/dataset/Case_study_3_PCA_of_site-exciton_model_dynamics/17110592 opens in new tab/window

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.

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