4 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…
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…
The Good, the Bad, and the Ugly of Atomistic Learning for "Clusters-to-Bulk" Generalization
MikoÅaj J. Gawkowski, Mingjia Li, Benjamin X. Shi +1
Training machine learning interatomic potentials (MLIPs) on total energies of molecular clusters using differential or transfer learning is becoming a popular route to extend the a…