摘要
Machine learning interatomic potentials are revolutionizing large-scale,accurate atomistic modeling in material science and chemistry.Many potentials use atomic cluster expansion or equivariant message-passing frameworks.Such frameworks typically use spherical harmonics as angular basis functions,followed by Clebsch-Gordan contraction to maintain rotational symmetry.We propose a mathematically equivalent and simple alternative that performs all operations in the Cartesian coordinates.This approach provides a complete set of polynormially independent features of atomic environments while maintaining interaction body orders.Additionally,we integrate low-dimensional embeddings of various chemical elements,trainable radial channel coupling,and inter-atomic message passing.The resulting potential,named Cartesian Atomic Cluster Expansion(CACE),exhibits good accuracy,stability,and generalizability.Wevalidate its performance in diverse systems,including bulk water,small molecules,and 25-element high-entropy alloys.
