Code for accelerating the calculation of electron-phonon coupling strength with machine learning

HamEPC is a machine learning workflow that leverages the HamGNN framework to efficiently calculate the electron-phonon coupling (EPC). By utilizing atomic orbital-based Hamiltonian matrices and gradients predicted by HamGNN, HamEPC is able to significantly accelerate EPC calculations compared to traditional density functional perturbation theory (DFPT) methods. HamEPC can be employed to evaluate important materials properties, including the electron-phonon coupling matrix, carrier mobility, and superconducting transition temperature.