A QMC-deep learning method for diffusivity estimation in random domains
Abstract
Exciton diffusion plays a vital role in the function of many organic semiconducting opto-electronic devices, where an accurate description requires precise control of heterojunctions. This poses a challenging problem because the parameterization of heterojunctions in high-dimensional random space is far beyond the capability of classical simulation tools. Here, we develop a novel method based on quasi-Monte Carlo sampling to generate the training data set and deep neural network to extract a function for exciton diffusion length on surface roughness with high accuracy and unprecedented efficiency, yielding an abundance of information over the entire parameter space. Our method provides a new strategy to analyze the impact of interfacial ordering on exciton diffusion and is expected to assist experimental design with tailored opto-electronic functionalities.
Cite
@article{arxiv.1910.14209,
title = {A QMC-deep learning method for diffusivity estimation in random domains},
author = {Liyao Lyu and Zhiwen Zhang and Jingrun Chen},
journal= {arXiv preprint arXiv:1910.14209},
year = {2020}
}