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We propose a machine learning method for predicting polarizabilities with thegoal of providing Raman spectra from molecular dynamics trajectories at reducedcomputational cost. A linear-response model is used as a first step andsymmetry-adapted machine learning is employed for the higher-ordercontributions as a second step. We investigate the performance of the approachfor several systems including molecules and extended solids. The method canreduce training set sizes required for accurate dielectric properties and Ramanspectra in comparison to a single-step machine learning approach.

Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra