6 citations · 10 across the 13 of their papers we have counts for
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How Good Is DFT for Solid-Liquid Interfaces? A Comparison With the Random-Phase Approximation for Water on Graphene
Xavier R. Advincula, Yair Litman, Jiuyang Shi +3
Simulations of water at solid interfaces routinely disagree on basic properties, from contact angle to friction, depending on the model used, whether an empirical force field or a…
Breaking Water at Graphene Defects
Samuel G. H. Brookes, IniOluwa C. Popoola, Fabian Berger +3
Water dissociation at solid surfaces underpins processes ranging from corrosion and catalysis to electrochemistry and photovoltaics. Defects often serve as reactive sites for disso…
Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models
Steffen Wedig, Felix Burton, Rokas Elijošius +2
Foundation machine-learned interatomic potentials (MLIPs) are trained on large quantum-mechanical datasets and generalise across broad regions of chemical and configurational space…
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…
Cutting Through the Noise: On-the-fly Outlier Detection for Robust Training of Machine Learning Interatomic Potentials
Terry C. W. Lam, Niamh O'Neill, Christoph Schran +1
The accuracy of machine learning interatomic potentials suffers from reference data that contains numerical noise. Often originating from unconverged or inconsistent electronic-str…
Strain-Dependent Wetting of Graphene
Darren Wayne Lim, Xavier R. Advincula, William C. Witt +3
Understanding how water wets graphene is critical for predicting and controlling its behaviour in nanofluidic, sensing, and energy applications. A key measure of wetting is the con…