21 – 22 September 2023

University of Birmingham, UK

Supported by The Heilbronn Institute Small Grants Scheme

This workshop aims to bring together researchers from applied mathematics and computational chemistry working on machine learning methods and related computational approaches for (or with applications in) materials science. A particular focus of this workshop will be on methods and mathematical theory that relate to the learning and simulation of (stochastic) dynamics of particle systems including dynamics-preserving coarse-graining techniques, learnable equivariant representations of physical quantities beyond machine-learned interatomic potentials (MLIP), and algorithm design for efficient numerical simulation of relevant dynamics.

Organiser:

Matthias Sachs (Birmingham)

Participants:

Lyudmila Grigoryeva (St. Gallen)
Eric Hall (Dundee)
Thomas Hudson (Warwick)
Jiahua Jiang (Birmingham)
Benedict Leimkuhler (Edinburgh)
Reinhard Maurer (Warwick)
Dominic Phillips (Edinburgh)
Pranav Singh (Bath)
Matthias Sachs (Birmingham)
Xiaocheng Shang (Birmingham)
Yue Wu (Strathclyde)

More information on the workshop website