SUPR
Machine learning potentials for atomistic modelling
Dnr:

NAISS 2025/23-177

Type:

NAISS Small Storage

Principal Investigator:

Carl Olsson

Affiliation:

Lunds universitet

Start Date:

2025-03-29

End Date:

2026-04-01

Primary Classification:

10304: Condensed Matter Physics

Webpage:

Allocation

Abstract

The purpose of this project is generate and test machine learning potentials based on the atomic cluster expansion (ACE) formalism, and its recent expansion - graph ACE (GRACE). Specifically, we aim to generate binary potentials for the W-He and Y-H systems, which are of relevance for nuclear reactor applications. We have generated datasets for training and evaluation by means of DFT modelling already. With this application, we apply for time to use GPUs for the training and testing the potentials.