129 citations · 141 across the 8 of their papers we have counts for
8 papers
Autonomous interpretation of atomistic scattering data
Andy S. Anker, John L. A. Gardner, Louise A. M. Rosset +2
Materials with bespoke properties have long been identified by computational searches, and their experimental realisation is now coming within reach through autonomous laboratories…
Distillation of atomistic foundation models across architectures and chemical domains
John L. A. Gardner, Daniel F. Thomas du Toit, Chiheb Ben Mahmoud +8
Machine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trai…
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…
An automated framework for exploring and learning potential-energy surfaces
Yuanbin Liu, Joe D. Morrow, Christina Ertural +6
Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing mach…
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…
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…