Electronic Response Quantities of Solids and Deep Learning
Abstract
We introduce a deep neural network (DNN) framework called the \textbf{r}eal-space \textbf{a}tomic \textbf{d}ecomposition \textbf{net}work (\textsc{radnet}), which is capable of making accurate polarization and static dielectric function predictions for solids. We use these predictions to calculate Born-effective charges, longitudinal optical transverse optical (LO-TO) splitting frequencies, and Raman tensors for two prototypical examples: GaAs and BN. We then compute the Raman spectra, and find excellent agreement with \textit{ab initio} techniques. \textsc{radnet} is as good or better than current methodologies. Lastly, we discuss how \textsc{radnet} scales to larger systems, paving the way for predictions of response functions on meso-scale structures with \textit{ab initio} accuracy.
Cite
@article{arxiv.2108.07614,
title = {Electronic Response Quantities of Solids and Deep Learning},
author = {Kevin Ryczko and Olivier Malenfant-Thuot and Michel Côté and Isaac Tamblyn},
journal= {arXiv preprint arXiv:2108.07614},
year = {2021}
}