20 papers
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
MikoÅaj J. Gawkowski, Nongnuch Artrith, Silvia Bonfanti +14
Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials…
Controlling the phase behaviour of ultraconfined water via bilayer graphene stacking
Yixuan Pu, Benjamin X. Shi, Pavan Ravindra +3
Water confined within nanoscale capillaries exhibits phase behaviour and transport properties that differ substantially from bulk, and these effects are commonly interpreted as con…
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
Isabel Creed, Tim Rein, Ingvars Vitenburgs +21
Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale a…
Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry
Sijia Chen, Niamh O'Neill, Benjamin X. Shi +1
Accurate machine learning interatomic potentials (MLIPs) have made first-principles-quality potential energy surfaces increasingly accessible for condensed-phase chemistry, but the…
General Learning of the Electric Response of Inorganic Materials
Bradley A. A. Martin, Alex M. Ganose, Venkat Kapil +2
We introduce \texttt{MACE-Field}, a field-aware, -equivariant interatomic potential that learns a single electric enthalpy functional an…
Rigorous Quantum Thermodynamics from Entropic Path Integral Coarse-Graining
Jing Shen, Ziyan Ye, Ming-Zheng Du +5
Nuclear quantum effects (NQEs) remain a major challenge for molecular simulations, as rigorous treatment requires imaginary-time path-integral methods with heavy computational over…