1 research outputs found
A QMC-deep learning method for diffusivity estimation in random domains
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