Deep-Learning Approach to First-Principles Transport Simulations

Large-scale first-principles transport calculations, while essential for device modeling, remain computationally demanding. To overcome this bottle neck, we combine first-principles transport calculations with machine learning-based nonlinear regression. We calculate the electronic conductance through first-principles based nonequilibrium Green's function techniques for small systems and map the transport properties onto local properties using local descriptors. We show that using the local descriptor as input features for deep learning-based nonlinear regression allows us to build a robust neural network that can predict the conductance of large systems beyond that of the current state-of-the-art first-principles calculation algorithms. Our protocol is applied to alkali metal nanowires, i.e., potassium, which have unique geometrical and electronic properties and hence nontrivial transport properties. We demonstrate that within our approach we can achieve qualitative agreement with experiment at a fraction of the computational effort as compared to the direct calculation of the transport properties using conventional first-principles methods.Received 19 June 2020Accepted 18 March 2021DOI:https://doi.org/10.1103/PhysRevLett.126.177701© 2021 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasBallistic transportQuantum transportPhysical SystemsMolecular junctionsTechniquesMachine learningMolecular dynamicsMolecular orbital theoryNonequilibrium Green's functionCondensed Matter, Materials & Applied Physics