Some list of codes on github useful for understanding in context to ML
https://github.com/ks-korovina/chembo : Bayesian optimization framework for organic molecules with synthesizable recommendations
https://github.com/denoptim-project/DENOPTIM: DENOPTIM is a software package for de novo design and virtual screening of functional molecules of any kind.
https://github.com/aspuru-guzik-group/selfies : Robust representation of semantically constrained graphs, in particular for molecules in chemistry
http://moleculenet.ai/datasets-1: MoleculeNet​
https://github.com/tilde-lab/awesome-materials-informatics : Awesome Materials Informatics
MolMap: An Efficient Convolutional Neural Network Based Molecular Deep Learning Tool
Ketcher: Web-based molecule sketcher
RXNMapper:Extraction of organic chemistry grammar from unsupervised learning of chemical reactions
ORD: by Coley, MIT coupled with Ketcher!!
stk tool kit fo supramolecular molecules: https://github.com/JelfsMaterialsGroup/stk ; see documentation: https://stk.readthedocs.io/en/stable/index.html
cage prediction: see Jelfs materials group
Pywindow: see Jelfs materials group github
15.
Lectures:
http://www.ipam.ucla.edu/programs/workshops/machine-learning-for-many-particle-systems/?tab=schedule : ML at IPAM in UCLA
http://www.eng.cam.ac.uk/profiles/gc121 by Prof. G. Csayni.
Database informations:
Catalysis-Hub.org, an open electronic structure database for surface reactions; https://www.nature.com/articles/sdata201680
Surface energies of elemental crystals; https://www.nature.com/articles/sdata201680