1 citations · 1 across the 1 of their papers we have counts for
7 papers
How reproducible are first-principles simulations of liquid water?
Niamh ONeill, Benjamin X. Shi, William J. Baldwin +5
Liquid water is fundamentally important, and its accurate computer simulation has been the driving force for myriad methodological developments. Ab initio molecular dynamics with f…
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
William J. Baldwin, Ilyes Batatia, Martin Vondrák +4
Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field, the most commonly employed architectu…
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…
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…
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…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…