Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra

Manuel Grumet, Clara von Scarpatetti, Tomáš Bučko, David A. Egger

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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 languageEnglish
Pages (from-to)6464-6470
Number of pages7
JournalJournal of Physical Chemistry C
Volume128
Issue number15
DOIs
StatePublished - 18 Apr 2024

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