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Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization 被引量:1
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作者 Maituo Yu shuyang yang +1 位作者 Chunzhi Wu Noa Marom 《npj Computational Materials》 SCIE EI CSCD 2020年第1期163-168,共6页
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... 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. 展开更多
关键词 OPTIMIZATION HUBBARD PARAMETER
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