Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles

doi:10.1088/1674-1056/acb2c3

中国物理B ›› 2023, Vol. 32 ›› Issue (5): 57306-057306.doi: 10.1088/1674-1056/acb2c3

所属专题： SPECIAL TOPIC — Smart design of materials and design of smart materials

Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles

Xiaoyi Ma(马宵怡)¹, Yufeng Luo(罗宇峰)¹, Mengke Li(李梦可)¹, Wenyan Jiao(焦文艳)¹, Hongmei Yuan(袁红梅)¹, Huijun Liu(刘惠军)^1,†, and Ying Fang(方颖)^2,‡

1 Key Laboratory of Artificial Micro-and Nano-Structures of Ministry of Education and School of Physics and Technology, Wuhan University, Wuhan 430072, China;
2 School of Computer Science, Wuhan University, Wuhan 430072, China

收稿日期:2022-11-23 修回日期:2022-12-24 接受日期:2023-01-13 出版日期:2023-04-21 发布日期:2023-04-26
通讯作者: Huijun Liu, Ying Fang E-mail:phlhj@whu.edu.cn;fangying@whu.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 62074114). The numerical calculations in this work have been done on the platform in the Supercomputing Center of Wuhan University.

Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles

Xiaoyi Ma(马宵怡)¹, Yufeng Luo(罗宇峰)¹, Mengke Li(李梦可)¹, Wenyan Jiao(焦文艳)¹, Hongmei Yuan(袁红梅)¹, Huijun Liu(刘惠军)^1,†, and Ying Fang(方颖)^2,‡

1 Key Laboratory of Artificial Micro-and Nano-Structures of Ministry of Education and School of Physics and Technology, Wuhan University, Wuhan 430072, China;
2 School of Computer Science, Wuhan University, Wuhan 430072, China

Received:2022-11-23 Revised:2022-12-24 Accepted:2023-01-13 Online:2023-04-21 Published:2023-04-26
Contact: Huijun Liu, Ying Fang E-mail:phlhj@whu.edu.cn;fangying@whu.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 62074114). The numerical calculations in this work have been done on the platform in the Supercomputing Center of Wuhan University.

摘要/Abstract

摘要： The novel electronic properties of bilayer graphene can be fine-tuned via twisting, which may induce flat bands around the Fermi level with nontrivial topology. In general, the band structure of such twisted bilayer graphene (TBG) can be theoretically obtained by using first-principles calculations, tight-binding method, or continuum model, which are either computationally demanding or parameters dependent. In this work, by using the sure independence screening sparsifying operator method, we propose a physically interpretable three-dimensional (3D) descriptor which can be utilized to readily obtain the Γ-point gap of TBG at arbitrary twist angles and different interlayer spacings. The strong predictive power of the descriptor is demonstrated by a high Pearson coefficient of 99% for both the training and testing data. To go further, we adopt the neural network algorithm to accurately probe the flat bands of TBG at various twist angles, which can accelerate the study of strong correlation physics associated with such a fundamental characteristic, especially for those systems with a larger number of atoms in the unit cell.

关键词: twisted bilayer graphene, band gap, flat bands, machine learning

Abstract: The novel electronic properties of bilayer graphene can be fine-tuned via twisting, which may induce flat bands around the Fermi level with nontrivial topology. In general, the band structure of such twisted bilayer graphene (TBG) can be theoretically obtained by using first-principles calculations, tight-binding method, or continuum model, which are either computationally demanding or parameters dependent. In this work, by using the sure independence screening sparsifying operator method, we propose a physically interpretable three-dimensional (3D) descriptor which can be utilized to readily obtain the Γ-point gap of TBG at arbitrary twist angles and different interlayer spacings. The strong predictive power of the descriptor is demonstrated by a high Pearson coefficient of 99% for both the training and testing data. To go further, we adopt the neural network algorithm to accurately probe the flat bands of TBG at various twist angles, which can accelerate the study of strong correlation physics associated with such a fundamental characteristic, especially for those systems with a larger number of atoms in the unit cell.

Key words: twisted bilayer graphene, band gap, flat bands, machine learning

中图分类号: (Electronic structure of nanoscale materials and related systems)

73.22.-f

71.15.-m (Methods of electronic structure calculations)

Xiaoyi Ma(马宵怡), Yufeng Luo(罗宇峰), Mengke Li(李梦可), Wenyan Jiao(焦文艳), Hongmei Yuan(袁红梅), Huijun Liu(刘惠军), and Ying Fang(方颖). Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles[J]. 中国物理B, 2023, 32(5): 57306-057306.

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Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles

Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles

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