Deep-learning density functional perturbation theory

Abstract

Calculating perturbation response properties of materials from first principles provides a vital link between theory and experiment, but is bottlenecked by the high computational cost. Here a general framework is proposed to perform density functional perturbation theory (DFPT) calculations by neural networks, greatly improving the computational efficiency. Automatic differentiation is applied on neural networks, facilitating accurate computation of derivatives. High efficiency and good accuracy of the approach are demonstrated by studying electron-phonon coupling and related physical quantities. This work brings deep-learning density functional theory and DFPT into a unified framework, creating opportunities for developing ab initio artificial intelligence.

References in corpus (4)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• Nearsightedness of Electronic Matter
• Lattice Dynamics Calculations based on Density-functional Perturbation Theory in Real Space
• Van der Waals Coefficients of Atoms and Molecules from a Simple Approximation for the Polarizability