TY - JOUR
T1 - A QMC-Deep Learning Method for Diffusivity Estimation in Random Domains
AU - Lyu , Liyao
AU - Zhang , Zhiwen
AU - Chen , Jingrun
JO - Numerical Mathematics: Theory, Methods and Applications
VL - 4
SP - 908
EP - 927
PY - 2020
DA - 2020/06
SN - 13
DO - http://doi.org/10.4208/nmtma.OA-2020-0032
UR - https://global-sci.org/intro/article_detail/nmtma/16959.html
KW - Exciton diffusion length, deep learning, Quasi-Monte Carlo sampling, diffusion
equation.
AB - <p style="text-align: justify;">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.</p>