Performant implementation of the atomic cluster expansion (PACE): Application to copper and silicon

2021/03/01 by Yury Lysogorskiy, Cas van der Oord, Lysogorskiy, Yury +19 · 32 citations
Materials Science · Physics and Astronomy · #Algorithm #Atomic physics #Atomic units #Benchmark (surveying) #Cluster (spacecraft) #Cluster expansion #Computational Physics (physics.comp-ph) #Computational science #Computer science #Copper #Coupled cluster #Electron and X-Ray Spectroscopy Techniques #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Materials science #Metallurgy #Molecule #Optoelectronics #Pace #Physics #Quantum mechanics #Silicon #Statistical physics #Surface and Thin Film Phenomena #cond-mat.mtrl-sci #physics.comp-ph

paper · pdf · doi:10.48550/arxiv.2103.00814

published in arXiv (Cornell University) (Cornell University)

arxiv created 2021/03/01 · arxiv updated 2021/03/02

Abstract

The atomic cluster expansion is a general polynomial expansion of the atomic energy in multi-atom basis functions. Here we implement the atomic cluster expansion in the performant C++ code \verb+PACE+ that is suitable for use in large scale atomistic simulations. We briefly review the atomic cluster expansion and give detailed expressions for energies and forces as well as efficient algorithms for their evaluation. We demonstrate that the atomic cluster expansion as implemented in \verb+PACE+ shifts a previously established Pareto front for machine learning interatomic potentials towards faster and more accurate calculations. Moreover, general purpose parameterizations are presented for copper and silicon and evaluated in detail. We show that the new Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations.

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