A machine learning enabled computational approach has been developed to accurately predict the equilibrium degree of inversion in spinel lattice and some magnetic properties of cobalt ferrite(CoFe_(2)O_(4))crystal.The...A machine learning enabled computational approach has been developed to accurately predict the equilibrium degree of inversion in spinel lattice and some magnetic properties of cobalt ferrite(CoFe_(2)O_(4))crystal.The computational approach is composed of construction of a database from density functional theory calculations,training of machine learning models,and atomistic simulations.Support vector regression was employed to derive the relation between system energy and atomic structures of CoFe_(2)O_(4).Using this trained machine learning model,atomistic Monte Carlo simulations predicted the equilibrium degree of inversion of CoFe_(2)O_(4)to be 0.755 at 1237 K.The strength of twenty-three types of superexchange interactions were determined using the linear regression model and further applied in magnetic Monte Carlo simulations to predict the Curie temperature of CoFe_(2)O_(4)to be 914 K.The predictions from the presented computational approach are well validated by the results from neutron diffraction measurement on CoFe_(2)O_(4).展开更多
基金the supports from U.S.National Science Foundation(NSF DMR#1905572 and NSF CMMI#1760916)support from the Office of Naval Research(ONR GRANT#N000142112498)+1 种基金supported in part by the University of Pittsburgh Center for Research Computing,RRID:SCR_022735,through the computer resources provided.Specifically,this work used the H2P clustersupported by NSF award number OAC-2117681.A portion of this research used resources at the High Flux Isotope Reactor,a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory.
文摘A machine learning enabled computational approach has been developed to accurately predict the equilibrium degree of inversion in spinel lattice and some magnetic properties of cobalt ferrite(CoFe_(2)O_(4))crystal.The computational approach is composed of construction of a database from density functional theory calculations,training of machine learning models,and atomistic simulations.Support vector regression was employed to derive the relation between system energy and atomic structures of CoFe_(2)O_(4).Using this trained machine learning model,atomistic Monte Carlo simulations predicted the equilibrium degree of inversion of CoFe_(2)O_(4)to be 0.755 at 1237 K.The strength of twenty-three types of superexchange interactions were determined using the linear regression model and further applied in magnetic Monte Carlo simulations to predict the Curie temperature of CoFe_(2)O_(4)to be 914 K.The predictions from the presented computational approach are well validated by the results from neutron diffraction measurement on CoFe_(2)O_(4).
