129 citations · 141 across the 8 of their papers we have counts for
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physics.chem-ph2022★ 2 cited
Synthetic data enable experiments in atomistic machine learning
John L. A. Gardner, Zoé Faure Beaulieu, Volker L. Deringer
Machine-learning models are increasingly used to predict properties of atoms in chemical systems. There have been major advances in developing descriptors and regression frameworks…
physics.chem-ph2022★ 129 cited
How to validate machine-learned interatomic potentials
Joe D. Morrow, John L. A. Gardner, Volker L. Deringer
Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy. With the growing availability of these methods there arises a need…