When: 31st March 2025 12:00 – 4th April 2025 14:00
Where: Daresbury Laboratory, in person event
- Fee: £350 (covers 4 nights accommodation and catering) – if we secure additional sponsorship this fee will be reduced
- Pre-requisites: Students will be expected to bring their own laptop, to have a decent level of coding experience (see pre-requisites below) and provide a letter of support from their supervisor
Description
This machine learning for materials training course is being run by AIchemy Hub in collaboration with Physical Sciences Data Infrastructure (PSDI) initiative with support from STFC-SCD, PSDS, CCP5 and CCP9 as a follow up to the very popular 2023 Machine learning for Atomistic Modelling Autumn School. 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.
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
For more information and to register visit https://aichemy.ac.uk/ml-training-school/