@Article{NMTMA-13-908, author = {Lyu , LiyaoZhang , Zhiwen and Chen , Jingrun}, title = {A QMC-Deep Learning Method for Diffusivity Estimation in Random Domains}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2020}, volume = {13}, number = {4}, pages = {908--927}, 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.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.OA-2020-0032}, url = {http://global-sci.org/intro/article_detail/nmtma/16959.html} }