8 citations · 17 across the 5 of their papers we have counts for
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physics.chem-ph2023★ 1 cited
Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces
Jonas Busk, Mikkel N. Schmidt, Ole Winther +2
Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting an…
physics.chem-ph2022★ 8 cited
Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials
Mathias Schreiner, Arghya Bhowmik, Tejs Vegge +2
Machine Learning (ML) models have, in contrast to their usefulness in molecular dynamics studies, had limited success as surrogate potentials for reaction barrier search. It is due…