Anharmonic Raman spectra simulation of crystals from deep neural networks

Abstract

Raman spectroscopy is an effective tool to analyze the structures of various materials as it provides chemical and compositional information. However, the computation demands for Raman spectra are typically significant because quantum perturbation calculations need to be performed beyond ground state calculations. This work introduces a novel route based on deep neural networks (DNNs) and density-functional perturbation theory to access anharmonic Raman spectra for extended systems. Both the dielectric susceptibility and the potential energy surface are trained using DNNs. The ab initio anharmonic vibrational Raman spectra can be reproduced well with machine learning and DNNs. Silicon and paracetamol crystals are used as showcases to demonstrate the computational efficiency.