Conference Template

Machine Learning for Quantum Matter

The Machine Learning for Quantum Matter workshops are intended to help a build a research community in the area of applying machine learning to accelerating areas of quantum chemistry and computational physics. We are a community of research groups interested in research in this area, organising events at cost price. We are particularly interested in nurturing the early career researchers in this area: please do not feel put off by the sometimes high falutin nature of the research talks - you are very welcome here!

Our next event will be on Friday 3rd May 2024 at Imperial College London.

Lectures will run from 10AM to 5PM. Talks will be 30 minutes, with 10 minutes for Q&A and AV faff.

Our provisional timetable is:

10:00 Ilyses Batatia (University of Cambridge)
Foundation models for materials and molecular chemistry
10:40 Gino Cassella (Imperial College London)
Freedom from basis sets with Neural Network Variational Monte Carlo
11:20 Twenty minute break.
11:40 Livia Bartok-Partay (Warwick)
Computational thermodynamics: What can the unbiased sampling of the configuration space tell us?
12:20 Evan Sheridan (Phasecraft)
Variational quantum simulation of many body problems
13:00 One hour lunch break.
14:00 Emma King-Smith (University of Cambridge)
Practical Machine Learning for Organic Small Molecule Modelling
14:40 Tong Wan 'Tonny' Lou (Imperial College London)
Neural Wave Functions for Superfluids
15:40 Harry Moore (University of Cambridge)
Transferrable machine learned forcefields for biomolecular simulation
15:20 Twenty minute break.
16:20 Zsuzsanna Koczor-Benda (Warwick)
Generative inverse design of functional molecules for plasmonic nanodevices

Organisers

The Machine Learning for Quantum Matter workshops are organised by Roberto Bondesan, Jarvist Moore Frost, Mohammed Azzouzi, Alexander Ganose and Keith Butler.

The workshops are supported by Imperial’s Department of Chemistry, and Imperial I-X.