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physics.comp-ph2024
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…
physics.comp-ph2024★ 1 cited
Hyperparameter Optimization for Atomic Cluster Expansion Potentials
Daniel F. Thomas du Toit, Yuxing Zhou, Volker L. Deringer
Machine-learning-based interatomic potentials enable accurate materials simulations on extended time- and lengthscales. ML potentials based on the Atomic Cluster Expansion (ACE) fr…