Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra
Manuel Grumet, Clara von Scarpatetti, Tomáš Bučko, David A. Egger
Abstract
High Resolution Image Download MS PowerPoint Slide Raman spectroscopy is an important characterization tool with diverse applications in many areas of research. We propose a machine learning (ML) method for predicting polarizabilities with the goal of providing Raman spectra from molecular dynamics trajectories at a reduced computational cost. A linear-response model is used as a first step, and symmetry-adapted ML is employed for the higher-order contributions as a second step. We investigate the performance of the approach for several systems, including molecules and extended solids. The method can reduce the training-set sizes required for accurate dielectric properties and Raman spectra in comparison to a single-step ML approach.