The properties of electrons in matter are of fundamental importance.They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior o...The properties of electrons in matter are of fundamental importance.They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets.Modeling and simulation of such diverse applications rely primarily on density functional theory(DFT),which has become the principal method for predicting the electronic structure of matter.While DFT calculations have proven to be very useful,their computational scaling limits them to small systems.We have developed a machine learning framework for predicting the electronic structure on any length scale.It shows up to three orders of magnitude speedup on systems where DFT is tractable and,more importantly,enables predictions on scales where DFT calculations are infeasible.Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.展开更多
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This work was in part supported by the Center for Advanced Systems Understanding(CASUS)which is financed by Germany’s Federal Ministry of Education and Research(BMBF)and by the Saxon state government out of the State budget approved by the Saxon State Parliament.
文摘The properties of electrons in matter are of fundamental importance.They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets.Modeling and simulation of such diverse applications rely primarily on density functional theory(DFT),which has become the principal method for predicting the electronic structure of matter.While DFT calculations have proven to be very useful,their computational scaling limits them to small systems.We have developed a machine learning framework for predicting the electronic structure on any length scale.It shows up to three orders of magnitude speedup on systems where DFT is tractable and,more importantly,enables predictions on scales where DFT calculations are infeasible.Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.
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.
