Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics  

作　　者：Svetoslav Nikolov Mitchell AWood Attila Cangi Jean-Bernard Maillet Mihai-Cosmin Marinica Aidan PThompson Michael P.Desjarlais Julien Tranchida 

机构地区：[1]Computational Multiscale Department,Sandia National Laboratories,P.O.Box 5800,MS 1322,Albuquerque,NM 87185,USA [2]Center for Advanced Systems Understanding(CASUS),D-02826 Görlitz,Germany [3]Helmholtz-Zentrum Dresden-Rossendorf,D-01328 Dresden,Germany [4]CEA-DAM,DIF,Arpajon Cedex F-91297,France [5]UniversitéParis-Saclay,CEA,LMCE,91680 Bruyères-le-Châtel,France [6]UniversitéParis-Saclay,CEA,Service de Recherches de Métallurgie Physique,Gif-sur-Yvette 91191,France [7]Sandia National Laboratories,P.O.Box 5800,MS 1322,Albuquerque,NM 87185,USA

出　　处：《npj Computational Materials》2021年第1期1414-1425,共12页计算材料学（英文）

基　　金：All authors thank Mark Wilson for his detailed review and edits.Sandia 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.This paper describes objective technical results and analysis.Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S.Department of Energy or the United States Government.A.C.acknowledges funding from the Center for Advanced Systems Understanding(CASUS)which is financed by the German Federal Ministry of Education and Research(BMBF)and by the Saxon State Ministry for Science,Art,and Tourism(SMWK)with tax funds on the basis of the budget approved by the Saxon State Parliament.

摘　　要：A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials(ML-IAPs)for largescale spin-lattice dynamics simulations.The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP.Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed.Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations.We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP forα-iron.The combined potential energy surface yields excellent agreement with firstprinciples magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus,magnetization,and specific heat across the ferromagnetic–paramagnetic phase transition.

关 键 词：TRANSITION DYNAMICS MAGNETO 

分 类 号：O737[理学—晶体学]