Deep Learning-Based Quantum Transport Simulations in Two-Dimensional Materials

TL;DR

This paper introduces DeePTB-NEGF, a deep learning framework that accelerates quantum transport simulations in 2D materials, maintaining accuracy while significantly reducing computational costs, thus facilitating large-scale material and device studies.

Contribution

The paper presents a novel deep learning-based tight-binding approach integrated with quantum transport simulations, enabling fast and accurate predictions for 2D materials beyond traditional DFT methods.

Findings

Achieves excellent agreement with DFT-NEGF results in band structures and transmission spectra.

Provides orders-of-magnitude faster simulations compared to conventional methods.

Demonstrates applicability to graphene, h-BN, and MoS2 for large-scale studies.

Abstract

Two-dimensional (2D) materials exhibit a wide range of electronic properties that make them promising candidates for next-generation nanoelectronic devices. Accurate prediction of their quantum transport behavior is therefore of both fundamental and technological importance. While density functional theory (DFT) combined with the non-equilibrium Green$'$s function (NEGF) formalism provides reliable insights, its high computational cost limits applications to large-scale or high-throughput studies. Here we present DeePTB-NEGF, a framework that combines a deep learning-based tight-binding Hamiltonians derived learned directly from first-principles calculations (DeePTB) with efficient quantum transport simulations implemented in the DPNEGF package. To validate the method, we apply it to three prototypical 2D materials: graphene, hexagonal boron nitride (h-BN), and MoS$_2$. The resulting band structures and transmission spectra show excellent agreement with conventional DFT-NEGF results, while achieving orders-of-magnitude improvement in efficiency. These results highlight the capability of DeePTB-NEGF to enable accurate and efficient quantum transport simulations, thereby opening avenues for large-scale exploration and device design in 2D materials.