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 PACE that is suitable for use ...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 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 PACE shifts a previously established Pareto front for machine learning interatomic potentials toward faster and more accurate calculations.Moreover,general purpose parameterizations are presented for copper and silicon and evaluated in detail.We show that the Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations.展开更多
基金The authors acknowledge helpful discussions with Marc Cawkwell.R.D.acknowledges funding through the German Science Foundation(DFG),project number 405621217Sandia National Laboratories is a multimission laboratory managed and operated by National Technology&Engineering Solutions of Sandia,LLC,a wholly owned subsidiary of Honeywell International Inc.,for the U.S.Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
文摘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 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 PACE shifts a previously established Pareto front for machine learning interatomic potentials toward faster and more accurate calculations.Moreover,general purpose parameterizations are presented for copper and silicon and evaluated in detail.We show that the Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations.
