1 citations · 2 across the 2 of their papers we have counts for
2 papers
physics.comp-ph2023★ 1 cited
Synthetic pre-training for neural-network interatomic potentials
John L. A. Gardner, Kathryn T. Baker, Volker L. Deringer
Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity…
physics.chem-ph2023★ 1 cited
Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
Zoé Faure Beaulieu, Thomas C. Nicholas, John L. A. Gardner +2
Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB phases. We test the validity of this assumption by comparing simplified and fully ato…