Machine learning local potentials for accelerated electron-phonon interactions calculations within the projector augmented-wave framework
MLLocP is introduced, a machine learning strategy that learns the self-consistent local potential on real-space grids and supplies its displacement derivatives to the evaluation of all-electron PAW electron-phonon matrix elements, and validated for elemental Cu, polar BAs, and chemically disordered Cu3Au.