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.

翻译：二维材料的发展催生了日益复杂、高品质且种类繁多的新型化合物。全面定量理论的关键需求之一在于精确确定这些材料的能带结构参数。然而，由于能带结构复杂且实验探测手段具有间接性，这一任务颇具挑战。在本研究中，我们提出一个通用框架，利用深度神经网络从实验数据中推导能带结构参数。我们将该方法应用于三层石墨烯的穿透场电容测量——这是一种有效探测其态密度的技术。首先，我们证明训练好的深度网络能够准确预测作为紧束缚参数函数的穿透场电容。其次，我们利用训练网络的快速准确预测能力，直接从实验数据中自动确定紧束缚参数，所提取的参数与文献值吻合良好。最后，我们讨论了该方法在其他材料及穿透场电容之外的实验技术中的潜在应用。