3 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 (…
cond-mat.mtrl-sci2023
Developments and Further Applications of Ephemeral Data Derived Potentials
Pascal T. Salzbrenner, Se Hun Joo, Lewis J. Conway +5
Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), whic…
cond-mat.mes-hall2020
A universal signature in the melting of metallic nanoparticles
L. Delgado-Callico, K. Rossi, R. Pinto-Miles +2
Characterizing the melting behaviour of monometallic nanoparticles is a great challenge from both the experimental and the theoretical point of view. To this end, we disclose a uni…