
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.
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The accessibility metadata for this title is unknown or has not been provided to Elsevier by the original publisher.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
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
Case study 2
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.
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