Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization

Journal Article · 27 November 2020 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-020-00446-9 · OSTI ID:1724440

Yu, Maituo; Yang, Shuyang ; Wu, Chunzhi; Marom, Noa

Abstract 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.

• Sponsoring Organization: USDOE
• Grant/Contract Number: SC0019274
• OSTI ID: 1724440
• Journal Information: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 6; ISSN 2057-3960
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English