Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra

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.

Raman Spectra of Amino Acids and Peptides from Machine Learning Polarizabilities

“…Another ML method is the Gaussian process regressor (GPR), which uses kernel functions to describe the similarity between atomic configurations . Similarly to NN, GPR have been applied to obtain force fields, − as well as polarizabilities and Raman spectra. − For example, GPR has been combined with path-integral MD trajectories to obtain the vibrational spectra of water. , More recently, GPR was also extended for prediction of electronic densities and its response to electric field, which consequently allows one to obtain polarizabilities. − One remaining obstacle to ML-based methods is transferability, i.e., the reliability of the predictions when the model is applied to molecules outside of its training set. A model trained solely on paracetamol molecules can reasonably predict polarizabilities and Raman spectra of paracetamol crystals and BPM and GPR trained for alkanes of different sizes can also successfully predict the Raman spectra of large molecules .…”