1 citations · 2 across the 5 of their papers we have counts for
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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…
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…
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…
When Is Nanoconfined Water Different From Interfacial Water?
Xavier R. Advincula, Christoph Schran, Angelos Michaelides
Water behaves very differently at surfaces and under extreme confinement, but the boundary between these two regimes has remained unclear. Despite evidence that interfacial effects…
How Accurate Are DFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets
Domantas Kuryla, Fabian Berger, Gábor Csányi +1
Training of general-purpose machine learning interatomic potentials (MLIPs) relies on large datasets with properties usually computed with density functional theory (DFT). A pre-re…