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About

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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.

Learning outcomes

Awareness of the state-of-the-art methods for machine learning for atomistic and molecular simulations Hands on experience of using machine learning for atomistic and molecular simulations

Pre-requisites

Students attending this course must already have a foundational level of Python experience and hands on experience of using Python in their research. You will be expected to provide your own laptop for the training course, although software installation will not be required. A letter of support will be required from your supervisor alongside your application, this will be requested by email following your application. This letter of support is to show the backing of your supervisor to attend the training and must be completed for your application to be assessed.

Editions of the school

Sponsors

psdi Aichemy ccp5 cecam ccp9 cosec