摘要
Within density functional theory(DFT),adding a Hubbard U correction can mitigate some of the deficiencies of local and semi-local exchange-correlation functionals,while maintaining computational efficiency.However,the accuracy of DFT+U largely depends on the chosen Hubbard U values.We propose an approach to determining the optimal U parameters for a given material by machine learning.The Bayesian optimization(BO)algorithm is used with an objective function formulated to reproduce the band structures produced by more accurate hybrid functionals.This approach is demonstrated for transition metal oxides,europium chalcogenides,and narrow-gap semiconductors.The band structures obtained using the BO U values are in agreement with hybrid functional results.Additionally,comparison to the linear response(LR)approach to determining U demonstrates that the BO method is superior.
基金
Work on III-V semiconductors was funded by the National Science Foundation(NSF)through grant OISE-1743717
Work on transition metal oxides and Eu chalcogenides was funded by the U.S.Department of Energy through grant DE-SC0019274
This research used resources of the National Energy Research Scientific Computing Center(NERSC),a U.S.Department of Energy Office of Science User Facility operated under Contract No.DE-AC02-05CH11231.
