Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductors

Phonon-assisted optical absorption in semiconductors is crucial for understanding and optimizing optoelectronic devices, yet its accurate simulation remains a significant challenge in computational materials science. We present an efficient approach that combines deep learning tight-binding (TB) and potential models to efficiently calculate the phonon-assisted optical absorption in semiconductors with $ab$ $initio$ accuracy. Our strategy enables efficient sampling of atomic configurations through molecular dynamics and rapid computation of electronic structure and optical properties from the TB models. We demonstrate its efficacy by calculating the temperature-dependent optical absorption spectra and band gap renormalization of Si and GaAs due to electron-phonon coupling over a temperature range of 100-400 K. Our results show excellent agreement with experimental data, capturing both indirect and direct absorption processes, including subtle features like the Urbach tail. This approach offers a powerful tool for studying complex materials with high accuracy and efficiency, paving the way for high-throughput screening of optoelectronic materials.

Deep Learning Accelerated Quantum Transport Simulations in Nanoelectronics: From Break Junctions to Field-Effect Transistors

Quantum transport calculations are essential for understanding and designing nanoelectronic devices, yet the trade-off between accuracy and computational efficiency has long limited their practical applications. We present a general framework that combines the deep learning tight-binding Hamiltonian (DeePTB) approach with the non-equilibrium Green's Function (NEGF) method, enabling efficient quantum transport calculations while maintaining first-principles accuracy. We demonstrate the capabilities of the DeePTB-NEGF framework through two representative applications: comprehensive simulation of break junction systems, where conductance histograms show good agreement with experimental measurements in both metallic contact and single-molecule junction cases; and simulation of carbon nanotube field effect transistors through self-consistent NEGF-Poisson calculations, capturing essential physics including the electrostatic potential and transfer characteristic curves under finite bias conditions. This framework bridges the gap between first-principles accuracy and computational efficiency, providing a powerful tool for high-throughput quantum transport simulations across different scales in nanoelectronics.