Cartesian atomic cluster expansion for machine learning interatomic potentials  

作　　者：Bingqing Cheng 

机构地区：[1]Department of Chemistry,University of California,Berkeley,CA,USA [2]The Institute of Science and Technology Austria,Klosterneuburg,Austria

出　　处：《npj Computational Materials》2024年第1期1631-1640,共10页计算材料学(英文)

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.

关 键 词：CARTESIAN CLUSTER EXPANSION 

分 类 号：O15[理学—数学]