Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene
This work addresses a key problem in materials science for researchers needing precise band structure parameters, though it is incremental as it applies existing deep learning methods to a new domain.
The authors tackled the challenge of accurately determining band structure parameters from experimental data by introducing a deep learning framework, achieving good agreement with literature values for trilayer graphene.
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.