Abstract
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.
Original language | English |
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Pages (from-to) | 6464-6470 |
Number of pages | 7 |
Journal | Journal of Physical Chemistry C |
Volume | 128 |
Issue number | 15 |
DOIs | |
State | Published - 18 Apr 2024 |