2 papers
cond-mat.mtrl-sci2026
Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead
Tom Hellyar, Pascal T. Salzbrenner, Peter I. C. Cooke +3
We compare the predicted phase behaviour of lead (Pb) using three different interatomic potential models, including an embedded atom method (EAM), a modified embedded atom method (…
physics.chem-ph2025
Understanding and improving transferability in machine-learned activation energy predictors
Joe Gilkes, Mark Storr, Reinhard J. Maurer +1
The calculation of reactive properties is a challenging task in chemical reaction discovery. Machine learning (ML) methods play an important role in accelerating electronic structu…