Recommended literature and sources

Articles

General overviews

• Recent advances and applications of machine learning in solid-state materials science

Materials properties prediction

• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• Predicting the Curie temperature of ferromagnets using machine learning
• Discovery of high-entropy ceramics via machine learning
• Prediction of Large Magnetic Moment Materials With Graph Neural Networks and Random Forests

New materials prediction

• Inverse Design of Solid-State Materials via a Continuous Representation
• Machine-learning-assisted search for functional materials over extended chemical space
• Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Books

• Coming soon

Web-sites

• Deep Graph Library - DGL

Repositories

Deep graph learning

• DGL-official examples

Extremely Useful repos

• Materials-Related Databases

Tutorials

• Introduction to Pytorch geometric

Courses

• Crystal structure (open Cambridge course)
• Machine Learning with Graphs (open-source course, Standford - 2023)

Lectures

Deep graph learning

• Deep Graph Generative Models (Stanford University - 2019)

Materials Databases

• The Materials Project
• Open Materials DB
• The Open Quantum Materials DB
• The Material Properties Open Database
• AFLOW