Electronic Quantum Transport Modeling of Monolayer MoS2 Diodes Based on Machine Learning

Quantum transport simulation based on first principles has been widely applied to study the transport properties of nanoscale devices. However, such a method is computationally expensive to study large-scale systems, which becomes a key bottleneck in device simulations. To this end, we develop a deep neural network approach to accelerate quantum transport simulations., enabling faster transport calculations while maintaining accuracy for large-scale devices. We studied monolayer Mos2 diodes with different scales as the study system., mapped their atomic structure characteristics to local descriptors as the input of the neural network., and used the non-equilibrium Green's function-density functional theory (NEGF-DFT) method to calculate the electron transmission coefficient of the system as the output of prediction. Our experimental results show that for large-scale devices, quantum transport modeling based on machine learning can achieve acceptable computational efficiency, and our scheme provides a promising solution for breaking the accuracy-efficiency dilemma to achieve large-scale nanodevice simulation.