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 couplin...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.展开更多
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.
文摘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.
