13 papers
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…
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…
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…
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…