Machine-learned interatomic potentials for alloys and alloy phase diagrams

作     者：Conrad W.Rosenbrock Konstantin Gubaev Alexander VShapeev Livia B.Pártay Noam Bernstein Gábor Csányi Gus L.W.Hart 

作者机构：Department of Physics and AstronomyBrigham Young UniversityProvoUTUSA84602 Skolkovo Institute of Science and TechnologySkolkovo Innovation CenterNobel Str.3Moscow 143026Russia Department of ChemistryUniversity of ReadingWhiteknightsReading RG66ADUK Department of ChemistryUniversity of WarwickCoventry CV47ALUK US Naval Research LaboratoryCenter for Computational Materials ScienceWashingtonDC 20375USA Department of EngineeringUniversity of CambridgeTrumpington StreetCambridge CB21PZUK 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2021年第7卷第1期

页      面：139-147页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：C.W.R.and G.L.W.H.were supported under ONR(MURI N00014-13-1-0635) L.B.P.acknowledges support from the Royal Society through a Dorothy Hodgkin Research Fellowship N.B.acknowledges support from the US Office of Naval Research through the US Naval Research Laboratory’s core research program,and computer time from the US DoD’s High-Performance Computing Modernization Program Office at the Air Force Research Laboratory Supercomputing Resource Center A.V.S.was supported by the Russian Science Foundation(grant number 18-13-00479) 

主　　题：alloy alloys phase 

摘      要：We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of *** compare two different *** tensor potentials(MTPs)are polynomial-like functions of interatomic distances and *** Gaussian approximation potential(GAP)framework uses kernel regression,and we use the smooth overlap of atomic position(SOAP)representation of atomic neighborhoods that consist of a complete set of rotational and permutational invariants provided by the power spectrum of the spherical Fourier transform of the neighbor *** types of potentials give excellent accuracy for a wide range of compositions,competitive with the accuracy of cluster expansion,a benchmark for this *** both models are able to describe small deformations away from the lattice positions,SOAP-GAP excels at transferability as shown by sensible transformation paths between configurations,and MTP allows,due to its lower computational cost,the calculation of compositional phase *** the fact that both methods perform nearly as well as cluster expansion but yield off-lattice models,we expect them to open new avenues in computational materials modeling for alloys.