activity
20242026
most citedFalse Metallization in Short-Ranged Machine Learned Interatomic Potentials

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

physics.chem-ph2026

From Accurate Quantum Chemistry to Converged Thermodynamics for Ion Pairing in Solution

Niamh O'Neill, Benjamin X. Shi, William C. Witt +6

Quantitative prediction of thermodynamic properties in solution is essential for translating atomistic simulations into reliable chemical insight. As an exemplar system, the behavi…

physics.chem-ph20261 cited

False Metallization in Short-Ranged Machine Learned Interatomic Potentials

Isaac J. Parker, Mandy J. Hoffmann, William J. Baldwin +7

Machine learned interatomic potentials (MLIPs) have enabled atomistic simulations with ab initio accuracy for a fraction of the computational cost. However, many widely used MLIPs…

physics.chem-ph2026

MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry

Ilyes Batatia, William J. Baldwin, Domantas Kuryla +10

Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on loc…

physics.chem-ph2025

Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water

Niamh O'Neill, Benjamin X. Shi, William Baldwin +5

Simulating liquid water to an accuracy that matches its wealth of available experimental data requires both precise electronic structure methods and reliable sampling of nuclear (q…

cond-mat.mtrl-sci2024

Symmetry Breaking in the Superionic Phase of Silver-Iodide

Amir Hajibabaei, William J. Baldwin, Gábor Csányi +1

In the superionic phase of silver iodide, we observe a distorted tetragonal structure characterized by symmetry breaking in the cation distribution. This phase competes with the we…