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, CCP5 and CCP9, IOP Neuromorphic compute group and DAEMON cost network. 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
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.
NOTE we are aware of various scam emails going around offering accommodation and various help. Please ignore them.
Timelines & Fees
The application deadline is 26 November 2026. Supervisors will be contacted for a letter of support following your application. All letters of support must be submitted by 6th December 2025.
Food and 5 nights accommodation (Waterside Hotel Warrington) is included in the £250 fee paid for this event, travel to Daresbury (and public transport to /from the lab) is not included and will need to be covered by the attendee. If we are able to secure additional sponsorship for this event we will reduce the fee.
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.
Key Dates
- Registration opening: 15th of October 2026
- Application deadline: 26th of November 2026, registration is closed.
- Acceptance decision: 19th of December 2026
- Accept place: 15th of January 2027
- Fee payments: 13th of February 2027
- Arrival: 4th of April 2027
- Departure: 9th of April 2027
Speakers & Lecturers
- Christoph Schran, University of Cambridge
- Nong Artrith, Utrecht University
- Will Baldwin, University of Cambridge
- Keith Butler, University College London
- Alin-Marin Elena, Scientific Computing Department, STFC
- Alex Ganose, Imperial College London
- Laura M. Helleckes, Imperial College London
- Lukas Hörmann, University of Vienna
- Ioan-Bogdan Magdău, Newcastle University
- Bradley Martin, University College London
Timetable: subject to change.
| Day | Time | Activity | Speaker(s) |
|---|---|---|---|
| Day 1 | 9:00-9:50 | Registration | |
| 9:50-10:00 | Welcome | ||
| 10:00-11:00 | Lecture 1 | ||
| 11:00-11:30 | Welcome | ||
| 11:30-12:30 | Lecture | ||
| 12:30-13:30 | Lunch | ||
| 13:30-15:00 | Introduction to Machine Learning | Lukas Hörmann | |
| 15:00-15:30 | Coffee Break | ||
| 15:30-17:00 | Practical Session | Lukas Hörmann | |
| 17:30-20:00 | Poster Session | ||
| Day 2 | 08:30-09:30 | Arrival, Coffee and Pastries | |
| 09:30-11:00 | Descriptors and Unsupervised Learning | Nong Artrith | |
| 11:00-11:30 | Coffee Break | ||
| 11:30-12:30 | Practical Session | Nong Artrith | |
| 12:30-13:30 | Lunch | ||
| 13:30-15:00 | Bayesian Optimisation | Laura Helleckes | |
| 15:00-15:30 | Coffee Break | ||
| 15:30-17:00 | Practical Session | Laura Helleckes | |
| 17:30-18:30 | Research Seminar 1 | Irea Mosquera-Lois (CuspAI) | |
| Day 3 | 08:30-09:30 | Arrival, Coffee and Pastries | |
| 09:30-11:00 | Neural Networks and Convolutional Neural Networks | Keith Butler | |
| 11:00-11:30 | Coffee Break | ||
| 11:30-12:30 | Practical Session | Keith Butler | |
| 12:30-13:30 | Lunch | ||
| 13:30-15:00 | Graph Neural Networks | Alex Ganose | |
| 15:00-15:30 | Coffee Break | ||
| 15:30-17:00 | Practical Session | Alex Ganose | |
| 17:30-20:00 | BBQ and Networking | ||
| Day 4 | 08:30-09:30 | Arrival, Coffee and Pastries | |
| 09:30-11:00 | Introduction to Machine-Learned Interatomic Potentials for Materials and Molecules | Will Baldwin | |
| 11:00-11:30 | Coffee Break | ||
| 11:30-12:30 | Practical Session | Will Baldwin & Alin Elena | |
| 12:30-13:30 | Lunch | ||
| 13:30-15:00 | Advanced MLIPs for Materials and Molecules | Will Baldwin | |
| 15:00-15:30 | Coffee Break | ||
| 15:30-17:00 | Practical Session | Will Baldwin & Alin Elena | |
| 17:30-18:30 | Research Seminar 2 | Christoph Schran | |
| Day 5 | 08:30-09:30 | Arrival, Coffee and Pastries | |
| 09:30-11:00 | Diffusion and Flow-Based Models | Bradley Martin | |
| 11:00-11:30 | Coffee Break | ||
| 11:30-12:30 | Practical Session | Bradley Martin | |
| 12:30-13:30 | Lunch | ||
| 13:30-15:00 | Autoregressive Models | Sean Kavanagh | |
| 15:00 onwards | Closing Remarks and Departure |
Contact
For inquires please write to Alin M Elena, alin-marin.elena@stfc.ac.uk