Anharmonic Raman spectra simulation of crystals from deep neural networks

AIP Advances 2021  ·  Honghui Shang, Haidi Wang ·

Raman spectroscopy is an effective tool to analyze the structures of various materials as it provides chemical and compositional information. However, the computation demands for Raman spectra are typically significant because quantum perturbation calculations need to be performed beyond ground state calculations. This work introduces a novel route based on deep neural networks (DNNs) and density-functional perturbation theory to access anharmonic Raman spectra for extended systems. Both the dielectric susceptibility and the potential energy surface are trained using DNNs. The ab initio anharmonic vibrational Raman spectra can be reproduced well with machine learning and DNNs. Silicon and paracetamol crystals are used as showcases to demonstrate the computational efficiency.

PDF
No code implementations yet. Submit your code now

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here