This organisation hosts resources for AI for Materials (MATE70026), a module in the Department of Materials at Imperial College London, and the accompanying book Introduction to Artificial Intelligence for Materials.
The material covers how composition, structure and property information can be represented for machine learning, and how to build, train, and evaluate models using open tools and public datasets. Recent developments, including generative models, large language models, and autonomous laboratories, are also discussed. Case studies and tutorials accompany the main text.
The course is aimed at senior undergraduate and junior postgraduate students. A basic working knowledge of Python and familiarity with materials chemistry or physics are assumed.
- Course website: https://aiformaterials.github.io/
- Lectures: https://aiformaterials.github.io/lectures/
- Labs: https://aiformaterials.github.io/labs/
Questions, corrections, and suggestions are welcome as issues on the relevant repository.
Unless stated otherwise, content is shared under a CC BY-NC-ND 4.0 licence.
The module is led by Aron Walsh.