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.
https://github.com/dralgroup/MLinQCbook22-CH01
https://github.com/ffshy/ChapterDFTCaseStudy
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
https://github.com/Cindy611/TDQD
https://github.com/Liu-group/MLbook
https://github.com/rosecers/unsupervised-ml
https://github.com/dralgroup/MLinQCbook22-NN
https://github.com/dralgroup/MLinQCbook22-NN
https://github.com/WeiLiangXMU/Bayesian-Inference
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
https://github.com/tongzhugroup/Chapter13-tutorial
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
https://github.com/brunocuevas/density-learning-tutorials
https://github.com/zylustc/Learning-Dipole-Moments-and-Polarizabilities
Chapter 20. Learning excited-state properties by Julia Westermayr, Pavlo O. Dral, Philipp Marquetand
http://mlatom.com/mlinqcbook22-mlesprops/
Code and tutorial: https://github.com/schnarc/SchNarc/tree/DipoleMoments_Spectra Data: https://bit.ly/3lnUaZb
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.
https://github.com/stefabat/MLWavefunction
Chapter 26. Analysis of nonadiabatic molecular dynamics trajectories by Yifei Zhu, Jiawei Peng, Hong Liu and Zhenggang Lan
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.