Machine Learning for Atomistic Modelling Autumn School 2023

• Date of Event: 18th 12:00 – 20th September 14:00
• Location: Daresbury Laboratory, in person event
• Fee: £100 (covers 2 nights accommodation and catering)
• 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 the Physical Sciences Data Infrastructure (PSDI) initiative in collaboration with PSDS, AI4SD, STFC-SCD and CCP5.This training is targeted towards PhD students, in particular those in the Materials and Molecular Simulations field. The aim of this training is to introduce attendees to the latest methods of machine learning applied to 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 atomic and molecular simulations
• Hands on experience of using machine learning for atomic and molecular simulations

Outline Agenda – Draft

Day 3 – 20th

• 09:00 – 10:30: Practical Session: Building and training GNN – Keith Butler & Alex Ganose
• 10:30 – 11:00: Coffee
• 11:00 – 12:30: Practical Session: Using pre trained networks – Keith Butler & Alex Ganose
• 12:30 – 14:00: Lunch & Departure

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 letter of support is to show the backing of your supervisor to attend the training and must be completed on headed paper, but does not need to be detailed. A template of the minimum required content is available in this word document.

Timelines

The application deadline is 30th June 2023 (including letter of support from your supervisor). Applications are now closed. You will be informed of the outcome of your application on 1st August, you will have to accept your place within 1 week and payment is required by 1st September. These timelines have been amended due to the exceptionally high number of applications we received. 

Food and 2 nights accommodation is included in the £100 fee paid for this event, travel to Daresbury is not included and will need to be covered by the attendee. Please note: places on this course are limited and in the event of oversubscription to the training course we will favour a diverse group of attendees.  

Organising Committee 

• Alin-Marin Elena, Scientific Computing Department STFC 
• Keith Butler, Queen Mary University London 
• Reinhard Maurer, University of Warwick 
• Kim Jelfs, Imperial College London 
• Alex Ganose, Imperial College London
• Simon Coles, University of Southampton 
• Samantha Kanza, University of Southampton 
• Nicola Knight, University of Southampton