CaMML – Chemistry and Materials Machine Learning School

CaMML – Chemistry and Materials Machine Learning School is an international training couse in machine learning for materials training course. It has as Physical Sciences Data Infrastructure (PSDI) initiative event with support from STFC-SCD, PSDS, CCP5 and CCP9 in September 2023. Since 2025 we have as a main partner in addition to PSDI, AIchemy Hub. This training is targeted towards PhD students, in particular those in the Materials and Molecular Simulations field, who have experience of coding but are not highly experienced with machine learning. The aim of this training is to introduce attendees to the latest methods of machine learning for the atomistic simulation of materials.

This training will encompass a number of talks and practical sessions, focusing on the basics of machine learning, machine learning interatomic potentials and graph neural networks. There will also be the opportunity for attendees to present a poster on their work.

The original “instigators” were Nicola Knight, University of Southampton, Kim Jelfs and Alex Ganose, Imperial College London, Reinhard Maurer, University of Warwick, Keith Butler, University College Lodon and Alin Elena, Scientific Computring Department, STFC.

More information can be found here.