Modeling of f-electron systems is challenging due to the complex interplay of the effects of spin–orbit coupling,electron–electron interactions,and the hybridization of the localized f-electrons with itinerant condu...Modeling of f-electron systems is challenging due to the complex interplay of the effects of spin–orbit coupling,electron–electron interactions,and the hybridization of the localized f-electrons with itinerant conduction electrons.This complexity drives not only the richness of electronic properties but also makes these materials suitable for diverse technological applications.In this context,we propose and implement a data-driven approach to aid the materials discovery process.By deploying state-of-the-art algorithms and query tools,we train our learning models using a large,simulated dataset based on existing actinide and lanthanide compounds.The machine-learned models so obtained can then be used to search for new classes of stable materials with desired electronic and physical properties.We discuss the basic structure of our f-electron database,and our approach towards cleaning and correcting the structure data files.Illustrative examples of the applications of our database include successful prediction of stable superstructures of double perovskites and identification of a number of physically-relevant trends in strongly correlated features of f-electron based materials.展开更多
基金This work is supported by the Institute for Materials Sciences(IMS),NSEC at LANL and by the U.S.D.O.E at LANL under Project No.20170680ER(T.A.)through the LANL LDRD program.Work at LANL was supported in part by U.S.DOE Basic Energy Sciences Core Program LANL E3B5(J.-X.Z.and A.V.B.)The work at Northeastern University is supported by the US Department of Energy,Office of Science,Basic Energy Sciences grant number DE-FG02-07ER46352benefitted from Northeastern University’s Advanced Scientific Computation Center(ASCC)and the NERSC supercomputing center through DOE grant number DE-AC02-05CH11231.
文摘Modeling of f-electron systems is challenging due to the complex interplay of the effects of spin–orbit coupling,electron–electron interactions,and the hybridization of the localized f-electrons with itinerant conduction electrons.This complexity drives not only the richness of electronic properties but also makes these materials suitable for diverse technological applications.In this context,we propose and implement a data-driven approach to aid the materials discovery process.By deploying state-of-the-art algorithms and query tools,we train our learning models using a large,simulated dataset based on existing actinide and lanthanide compounds.The machine-learned models so obtained can then be used to search for new classes of stable materials with desired electronic and physical properties.We discuss the basic structure of our f-electron database,and our approach towards cleaning and correcting the structure data files.Illustrative examples of the applications of our database include successful prediction of stable superstructures of double perovskites and identification of a number of physically-relevant trends in strongly correlated features of f-electron based materials.
