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<p>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 the Non-Equilibrium Green’s Function (NEGF) formalism coupled with Density Functional Theory (DFT) 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 Hamiltonian derived directly from first-principles calculations (DeePTB) with efficient quantum transport simulations implemented in the DPNEGF package. We validate the method on five prototypical 2D materials (graphene, hexagonal boron nitride (h-BN), <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\hbox {MoS}_2\)</EquationSource></InlineEquation>, <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\hbox {WS}_2\)</EquationSource></InlineEquation>, and black phosphorus) demonstrating excellent agreement with conventional DFT-NEGF for band structures and transmission spectra. Beyond single-material benchmarks, we showcase the framework’s versatility by exploring strain engineering (uniaxial strain on graphene and biaxial strain on <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(\hbox {MoS}_2\)</EquationSource></InlineEquation>), substitution doping in <InlineEquation ID="IEq4"><EquationSource Format="TEX">\(\hbox {MoS}_2\)</EquationSource></InlineEquation>, and current-voltage characteristics of a graphene field-effect transistor (FET). A scaling analysis reveals that DeePTB-NEGF can simulate systems with hundreds of atoms in minutes, achieving speed-ups of over <InlineEquation ID="IEq5"><EquationSource Format="TEX">\(700\times\)</EquationSource></InlineEquation> compared to DFT-NEGF for heterostructures such as graphene/h-BN/graphene. These results establish DeePTB-NEGF as a powerful tool for autonomous, high-throughput design of quantum transport in microscopic heterostructures, enabling rapid prototyping of next-generation 2D devices.</p>

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Deep learning-accelerated NEGF formalism for autonomous design of quantum transport in microscopic heterostructures

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  • 发表日期：

    2026年7月4日

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  • Beshir Awol

摘要

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 the Non-Equilibrium Green’s Function (NEGF) formalism coupled with Density Functional Theory (DFT) 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 Hamiltonian derived directly from first-principles calculations (DeePTB) with efficient quantum transport simulations implemented in the DPNEGF package. We validate the method on five prototypical 2D materials (graphene, hexagonal boron nitride (h-BN), \(\hbox {MoS}_2\), \(\hbox {WS}_2\), and black phosphorus) demonstrating excellent agreement with conventional DFT-NEGF for band structures and transmission spectra. Beyond single-material benchmarks, we showcase the framework’s versatility by exploring strain engineering (uniaxial strain on graphene and biaxial strain on \(\hbox {MoS}_2\)), substitution doping in \(\hbox {MoS}_2\), and current-voltage characteristics of a graphene field-effect transistor (FET). A scaling analysis reveals that DeePTB-NEGF can simulate systems with hundreds of atoms in minutes, achieving speed-ups of over \(700\times\) compared to DFT-NEGF for heterostructures such as graphene/h-BN/graphene. These results establish DeePTB-NEGF as a powerful tool for autonomous, high-throughput design of quantum transport in microscopic heterostructures, enabling rapid prototyping of next-generation 2D devices.