3 papers
cond-mat.mtrl-sci2026
Data-driven atomistic modelling of hybrid halide perovskite passivation
Laura-Bianca PaÅca, Henry J. Snaith, Volker L. Deringer
Molecular passivation of surface defects is key to improving the optoelectronic performance of hybrid halide perovskite materials, but the underlying atomistic mechanisms are incom…
physics.comp-ph2025
Distillation of atomistic foundation models across architectures and chemical domains
John L. A. Gardner, Daniel F. Thomas du Toit, Chiheb Ben Mahmoud +8
Machine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trai…
cond-mat.mtrl-sci2025
Machine-learning-driven modelling of amorphous and polycrystalline BaZrS
Laura-Bianca PaÅca, Yuanbin Liu, Andy S. Anker +2
The chalcogenide perovskite material BaZrS is of growing interest for emerging thin-film photovoltaics. Here we show how machine-learning-driven modelling can be used to desc…