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

作　　者：马宵怡 罗宇峰 李梦可 焦文艳 袁红梅 刘惠军 方颖 Xiaoyi Ma;Yufeng Luo;Mengke Li;Wenyan Jiao;Hongmei Yuan;Huijun Liu;Ying Fang(Key Laboratory of Artificial Micro-and Nano-Structures of Ministry of Education and School of Physics and Technology,Wuhan University,Wuhan 430072,China;School of Computer Science,Wuhan University,Wuhan 430072,China)

机构地区：[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

出　　处：《Chinese Physics B》2023年第5期32-36,共5页中国物理B（英文版）

基　　金：the National Natural Science Foundation of China(Grant No.62074114)。

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 theΓ-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 

分 类 号：TQ127.11[化学工程—无机化工] O469[理学—凝聚态物理] TP181[理学—电子物理学]