A machine learning framework for Raman spectrum prediction

Raman spectroscopy captures materials’ vibrational properties in the form of highly resolved fingerprints with characteristic peaks. However, connecting Raman spectra to underlying structural and chemical attributes can be nontrivial and computationally expensive. In this work, we apply machine learning methods to obtain Raman spectra from accessible structural and atomic properties. Using an objective function inspired by optimal transport, we first learn low-dimensional representations of Raman spectra which serve as effective prediction targets for machine learning from materials attributes. Due to limited available training data, we employ symmetry-constrained Euclidean neural networks, which have demonstrated success on related property prediction tasks, and evaluate our framework on both ab initio and experimental spectra with varying complexity. Our approach enables rapid prediction of Raman spectra from structures which accelerates the interpretation of Raman spectroscopy data.