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

Engineering and Technology

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

M. Yu, S. Yang, et al.

Explore a novel machine learning approach that employs Bayesian optimization to pinpoint optimal Hubbard *U* parameters in DFT+*U* calculations, achieving band structures on par with, or even superior to, traditional linear response methods. This exciting research was conducted by Maituo Yu, Shuyang Yang, Chunzhi Wu, and Noa Marom.

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Playback language: English
Abstract
This paper proposes a machine learning approach using Bayesian optimization (BO) to determine optimal Hubbard *U* parameters in DFT+*U* calculations. The method aims to reproduce band structures obtained from more accurate hybrid functionals, demonstrated for transition metal oxides, europium chalcogenides, and narrow-gap semiconductors. The results show that the BO method produces band structures comparable to or better than those obtained using the linear response (LR) method, with improved computational efficiency.
Publisher
npj Computational Materials
Published On
Nov 27, 2020
Authors
Maituo Yu, Shuyang Yang, Chunzhi Wu, Noa Marom
Tags
Bayesian optimization
Hubbard U parameters
DFT+U calculations
band structures
transition metal oxides
computational efficiency
hybrid functionals

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