The Chemical and Materials Machine Learning School is an intensive, week-long training programme held annually each spring at STFC Daresbury Laboratory. Designed for researchers, postgraduate students and professionals, the school provides a comprehensive introduction to the application of machine learning techniques in chemistry and materials science.
Participants engage in expert-led lectures, practical hands-on sessions, and collaborative activities aimed at developing both foundational knowledge and advanced technical skills.
Training Resources
Materials from the school are available here and cover a wide range of topics central to machine learning applications in chemistry and materials science. Themes include:
Core Concepts
- Basics of Machine Learning
- Unsupervised Machine Learning
- Neural Networks
- Convolutional Neural Networks
- Convolutional Filters
- Exercises
Advanced Topics
- Convolutional Graph Networks
- MACE in Practice
- Nudge Elastic Bands with MLIPS
- Autoencoders
- Autoregressive LLMs
- Diffusion Models
These resources provide valuable support for both participants and the wider community seeking to explore the intersection of AI, chemistry, and materials science.