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

Abstract

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.

Authors:
; ORCiD logo; ; ORCiD logo
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1724440
Grant/Contract Number:  
SC0019274
Resource Type:
Published Article
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Name: npj Computational Materials Journal Volume: 6 Journal Issue: 1; Journal ID: ISSN 2057-3960
Publisher:
Nature Publishing Group
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Yu, Maituo, Yang, Shuyang, Wu, Chunzhi, and Marom, Noa. Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization. United Kingdom: N. p., 2020. Web. https://doi.org/10.1038/s41524-020-00446-9.
Yu, Maituo, Yang, Shuyang, Wu, Chunzhi, & Marom, Noa. Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization. United Kingdom. https://doi.org/10.1038/s41524-020-00446-9
Yu, Maituo, Yang, Shuyang, Wu, Chunzhi, and Marom, Noa. Fri . "Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization". United Kingdom. https://doi.org/10.1038/s41524-020-00446-9.
@article{osti_1724440,
title = {Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization},
author = {Yu, Maituo and Yang, Shuyang and Wu, Chunzhi and Marom, Noa},
abstractNote = {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.},
doi = {10.1038/s41524-020-00446-9},
journal = {npj Computational Materials},
number = 1,
volume = 6,
place = {United Kingdom},
year = {2020},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1038/s41524-020-00446-9

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