Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene

Henderson, P. , Ghazaryan, A., Zibrov, A. A., Young, A. F. and Serbyn, M. (2023) Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene. Physical Review B, 108(12), 125411. (doi: 10.1103/PhysRevB.108.125411)

Abstract

The development of two-dimensional materials has resulted in a diverse range of novel, high-quality compounds with increasing complexity. A key requirement for a comprehensive quantitative theory is the accurate determination of these materials' band structure parameters. However, this task is challenging due to the intricate band structures and the indirect nature of experimental probes. In this work, we introduce a general framework to derive band structure parameters from experimental data using deep neural networks. We applied our method to the penetration field capacitance measurement of trilayer graphene, an effective probe of its density of states. First, we demonstrate that a trained deep network gives accurate predictions for the penetration field capacitance as a function of tight-binding parameters. Next, we use the fast and accurate predictions from the trained network to automatically determine tight-binding parameters directly from experimental data, with extracted parameters being in a good agreement with values in the literature. We conclude by discussing potential applications of our method to other materials and experimental techniques beyond penetration field capacitance.

• Item Type: Articles
• Additional Information: A.F.Y. acknowledges primary support from the Department of Energy under award DE-SC0020043, and additional support from the Gordon and Betty Moore Foundation under award GBMF9471 for group operations.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Henderson, Dr Paul
• Authors: Henderson, P., Ghazaryan, A., Zibrov, A. A., Young, A. F., and Serbyn, M.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Physical Review B
• Publisher: American Physical Society
• ISSN: 1098-0121
• ISSN (Online): 1550-235X
• Published Online: 11 September 2023
• Copyright Holders: Copyright © 2023 American Physical Society
• First Published: First published in Physical Review B 108(12): 125411
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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