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Permutation-adapted complete and independent basis for atomic cluster expansion descriptors
James M. Goff, Charles Sievers, Mitchell A. Wood +1
Atomic cluster expansion (ACE) methods provide a systematic way to describe particle local environments of arbitrary body order. For practical applications it is often required tha…
Machine Learning Interatomic Potential for Simulations of Carbon at Extreme Conditions
Jonathan T. Willman, Kien Nguyen-Cong, Ashley S. Williams +5
A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed for simulations of carbon at extreme pressures (up to 5 TPa) and temperatures (…
Training Data Selection for Accuracy and Transferability of Interatomic Potentials
David Montes de Oca Zapiain, Mitchell A. Wood, Nicholas Lubbers +3
Advances in machine learning (ML) techniques have enabled the development of interatomic potentials that promise both the accuracy of first principles methods and the low-cost, lin…