6 citations · 8 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Efficient first-principles modeling of complex molecular crystals at sub-chemical accuracy
Benjamin X. Shi, Kristina M. Herman, Flaviano Della Pia +5
Molecules can form myriad crystalline polymorphs, each with distinct properties affecting their performance across diverse applications, from pharmaceuticals to functional material…
Nuclear quantum effects induce superionic proton transport in nanoconfined water
Pavan Ravindra, Xavier R. Advincula, Benjamin X. Shi +3
Recent work has suggested that nanoconfined water may exhibit superionic proton transport at lower temperatures and pressures than bulk water. Using first-principles-level simulati…
On the increase of the melting temperature of water confined in one-dimensional nano-cavities
Flaviano Della Pia, Andrea Zen, Venkat Kapil +3
Water confined in nanoscale cavities plays a crucial role in everyday phenomena in geology and biology, as well as technological applications at the water-energy nexus. However, ev…