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
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
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
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
Case study 2
Case study 3
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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