Machine-learning potentials for crystal defects

R Freitas, Y Cao - MRS Communications, 2022 - Springer
Decades of advancements in strategies for the calculation of atomic interactions have
culminated in a class of methods known as machine-learning interatomic potentials …

BoostMD: Accelerated Molecular Sampling Leveraging ML Force Field Features

LL Schaaf, I Batatia, J Tilly, TD Barrett - … 2024 Workshop on Data-driven and … - openreview.net
Accurately modelling atomic-scale processes, such as protein folding and catalysis, is
crucial in computational biology, chemistry, and materials science. While machine learning …