https://github.com/dralgroup/MLinQCbook22
https://github.com/dralgroup/MLinQCbook22
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/dralgroup/MLinQCbook22
- Owner: dralgroup
- License: mit
- Created: 2022-02-17T03:58:46.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-08-14T09:49:49.000Z (almost 3 years ago)
- Last Synced: 2024-08-04T11:01:25.917Z (11 months ago)
- Size: 17.6 KB
- Stars: 22
- Watchers: 1
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-qc-courses - book materials for *Quantum Chemistry in the Age of Machine Learning*
README
# Mirror of the [companion website](https://www.elsevier.com/books-and-journals/book-companion/9780323900492) for Quantum Chemistry in the Age of Machine Learning edited by Pavlo O. Dral
[Quantum Chemistry in the Age of Machine Learning](https://www.elsevier.com/books/Quantum%20Chemistry%20in%20the%20Age%20of%20Machine%20Learning/9780323900492) (paperback ISBN: 9780323900492) is a book edited by [Pavlo O. Dral](http://dr-dral.com).
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.