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

TL;DR

This paper presents a deep learning framework that accurately extracts band structure parameters from experimental density of states data, demonstrated on trilayer graphene, enabling efficient analysis of complex 2D materials.

Contribution

It introduces a neural network-based method to derive band structure parameters directly from experimental measurements, improving accuracy and speed over traditional techniques.

Findings

Deep neural network accurately predicts penetration field capacitance from tight-binding parameters.

The method automatically determines tight-binding parameters from experimental data.

Extracted parameters align well with literature values.

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.