Presentation
Accelerating Materials Modelling with Machine Learning: Challenges and Opportunities
Presenters
DescriptionFirst-principles materials modelling software can accurately predict many materials properties, but requires the numerical solution of complex, non-linear partial differential equations. Solving these equations is computationally intensive, and first-principles simulations consume a significant fraction of HPC usage (e.g. ~40% of the UK ARCHER2 Tier-1 facility). In recent years, machine learning (ML) methods have been applied to some of these property simulations, to reduce the number of numerical evaluations. The vast materials parameter space means that devising a "universal" ML model is challenging. One promising alternative is to couple the ML and direct numerical simulations more tightly, training and using the ML "on-the-fly". We will discuss these challenges and opportunities, along with results from embedding Gaussian Process-based ML models in the popular CASTEP first-principles modelling software, to reproduce and predict atomic forces substantially faster and with controllable uncertainty.
TimeMonday, June 312:30 - 13:00 CEST
LocationHG E 3
Session Chair
Event Type
Minisymposium
Chemistry and Materials
Climate, Weather, and Earth Sciences
Engineering
Physics
Computational Methods and Applied Mathematics