Abstract

Simulation of mesoscopic nanostructures is a central challenge in condensed matter physics and device applications. First-principles methods provide accurate electronic structures but are computationally prohibitive for large systems, while empirical band theories are efficient yet limited by parameter fitting that neglects wavefunction information and often yields non-transferable parameters. We propose a methodology that bridges these approaches, achieving first-principles-level reliability with computational efficiency through a machine-learning-enabled tight-binding framework. Our approach starts with Wannier tight-binding (WTB) parameters from small nanostructures, which serve as training data for machine-learning (ML). To remove the gauge freedom of Wannier functions that obscures size- and geometry-dependent parameter trends, we construct gauge-independent (GI) bases and transform the WTB model into a gauge-independent WTB (GI-WTB) model. This enables robust parameter fitting and ML prediction of parameter variations, yielding the machine-learning GI-WTB (ML-GI-WTB) model. Applied to MoS₂ armchair-edge nanoribbons, the ML-GI-WTB model shows excellent agreement with first-principles results and enables reliable simulations of sub-µm-wide nanoribbons. This framework provides a scalable tool for predicting electronic properties of realistic nanostructures beyond the reach of conventional first-principles methods.

Abstract

Simulation of mesoscopic nanostructures is a central challenge in condensed matter physics and device applications. First-principles methods provide accurate electronic structures but are computationally prohibitive for large systems, while empirical band theories are efficient yet limited by parameter fitting that neglects wavefunction information and often yields non-transferable parameters. We propose a methodology that bridges these approaches, achieving first-principles-level reliability with computational efficiency through a machine-learning-enabled tight-binding framework. Our approach starts with Wannier tight-binding (WTB) parameters from small nanostructures, which serve as training data for machine-learning (ML). To remove the gauge freedom of Wannier functions that obscures size- and geometry-dependent parameter trends, we construct gauge-independent (GI) bases and transform the WTB model into a gauge-independent WTB (GI-WTB) model. This enables robust parameter fitting and ML prediction of parameter variations, yielding the machine-learning GI-WTB (ML-GI-WTB) model. Applied to MoS₂ armchair-edge nanoribbons, the ML-GI-WTB model shows excellent agreement with first-principles results and enables reliable simulations of sub-µm-wide nanoribbons. This framework provides a scalable tool for predicting electronic properties of realistic nanostructures beyond the reach of conventional first-principles methods.

Abstract

Simulation of mesoscopic nanostructures is a central challenge in condensed matter physics and device applications. First-principles methods provide accurate electronic structures but are computationally prohibitive for large systems, while empirical band theories are efficient yet limited by parameter fitting that neglects wavefunction information and often yields non-transferable parameters. We propose a methodology that bridges these approaches, achieving first-principles-level reliability with computational efficiency through a machine-learning-enabled tight-binding framework. Our approach starts with Wannier tight-binding (WTB) parameters from small nanostructures, which serve as training data for machine-learning (ML). To remove the gauge freedom of Wannier functions that obscures size- and geometry-dependent parameter trends, we construct gauge-independent (GI) bases and transform the WTB model into a gauge-independent WTB (GI-WTB) model. This enables robust parameter fitting and ML prediction of parameter variations, yielding the machine-learning GI-WTB (ML-GI-WTB) model. Applied to MoS₂ armchair-edge nanoribbons, the ML-GI-WTB model shows excellent agreement with first-principles results and enables reliable simulations of sub-µm-wide nanoribbons. This framework provides a scalable tool for predicting electronic properties of realistic nanostructures beyond the reach of conventional first-principles methods.

Abstract

Simulation of mesoscopic nanostructures is a central challenge in condensed matter physics and device applications. First-principles methods provide accurate electronic structures but are computationally prohibitive for large systems, while empirical band theories are efficient yet limited by parameter fitting that neglects wavefunction information and often yields non-transferable parameters. We propose a methodology that bridges these approaches, achieving first-principles-level reliability with computational efficiency through a machine-learning-enabled tight-binding framework. Our approach starts with Wannier tight-binding (WTB) parameters from small nanostructures, which serve as training data for machine-learning (ML). To remove the gauge freedom of Wannier functions that obscures size- and geometry-dependent parameter trends, we construct gauge-independent (GI) bases and transform the WTB model into a gauge-independent WTB (GI-WTB) model. This enables robust parameter fitting and ML prediction of parameter variations, yielding the machine-learning GI-WTB (ML-GI-WTB) model. Applied to MoS₂ armchair-edge nanoribbons, the ML-GI-WTB model shows excellent agreement with first-principles results and enables reliable simulations of sub-µm-wide nanoribbons. This framework provides a scalable tool for predicting electronic properties of realistic nanostructures beyond the reach of conventional first-principles methods.