129 citations · 141 across the 8 of their papers we have counts for
4 papers · 1 filter
Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials
Chiheb Ben Mahmoud, Zakariya El-Machachi, Krystian A. Gierczak +2
With the rapidly growing availability of machine-learned interatomic potential (MLIP) models for chemistry, much current research focuses on the development of generally applicable…
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…
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…
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…