Volume 3 Issue 2
June  2024
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Guanghui Cai, Zhendong Cao, Fankai Xie, Huaxian Jia, Wei Liu, Yaxian Wang, Feng Liu, Xinguo Ren, Sheng Meng, Miao Liu. Predicting structure-dependent Hubbard U parameters via machine learning[J]. Materials Futures, 2024, 3(2): 025601. doi: 10.1088/2752-5724/ad19e2
Citation: Guanghui Cai, Zhendong Cao, Fankai Xie, Huaxian Jia, Wei Liu, Yaxian Wang, Feng Liu, Xinguo Ren, Sheng Meng, Miao Liu. Predicting structure-dependent Hubbard U parameters via machine learning[J]. Materials Futures, 2024, 3(2): 025601. doi: 10.1088/2752-5724/ad19e2
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Predicting structure-dependent Hubbard U parameters via machine learning

© 2024 The Author(s). Published by IOP Publishing Ltd on behalf of the Songshan Lake Materials Laboratory
Materials Futures, Volume 3, Number 2
  • Received Date: 2023-11-02
  • Accepted Date: 2023-12-27
  • Rev Recd Date: 2023-12-21
  • Publish Date: 2024-01-24
doi: 10.1088/2752-5724/ad19e2
  • 1.

    Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China

  • 2.

    School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People’s Republic of China

  • 3.

    Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, People’s Republic of China

  • 4.

    Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China

  • 5.

    Tencent AI Lab, Tencent, Shenzhen 518057, People’s Republic of China

  • 6.

    Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT 84112, United States of America

  • Data availability statement

    Data will be available upon request. The code for BO and ML in this work can be found at: https://github.com/zdcao121/ml4dftu

    G Cai and Z Cao contributed equally to this work.

More Information
    Abstract
  • AbstractDFT + U is a widely used treatment in the density functional theory (DFT) to deal with correlated materials that contain open-shell elements, whereby the quantitative and sometimes even qualitative failures of local and semi-local approximations can be corrected without much computational overhead. However, finding appropriate U parameters for a given system and structure is non-trivial and computationally intensive, because the U value has generally a strong chemical and structural dependence. In this work, we address this issue by building a machine learning (ML) model that enables the prediction of material- and structure-specific U values at nearly no computational cost. Using Mn-O system as an example, the ML model is trained by calibrating DFT + U electronic structures with the hybrid functional results of more than 3000 structures. The model allows us to determine an accurate U value (MAE = 0.128 eV, R2 = 0.97) for any given Mn-O structure. Further analysis reveals that M-O bond lengths are key local structural properties in determining the U value. This approach of the ML U model is universally applicable, to significantly expand and solidify the use of the DFT + U method.
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    • Author contributions

    M L proposed and led this project. Z C and G C wrote the code. Z C performed the calculations and analyzed the results. X R and S M copiloted the project with important intellectual contributions. F X, H J, and W L provided assistance with the ML algorithm. Z C and M L wrote the manuscript. Y W, F L, X R, and S M reviewed and revised the manuscript. G C and Z C contributed equally to this work.

    Conflict of interest

    The authors declare no competing interests.

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