Interpretable deep learning for guided structure-property explorations in photovoltaics
Abstract
The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very expensive to evaluate, either experimentally or computationally. Hence, designing morphologies to achieve higher performances is non-trivial and often intractable. To solve this, we first introduce a deep convolutional neural network (CNN) architecture that can serve as a fast and robust surrogate for the complex structure-property map. Several tests were performed to gain trust in this trained model. Then, we utilize this fast framework to perform robust microstructural design to enhance device performance.
Cite
@article{arxiv.1811.06067,
title = {Interpretable deep learning for guided structure-property explorations in photovoltaics},
author = {Balaji Sesha Sarath Pokuri and Sambuddha Ghosal and Apurva Kokate and Baskar Ganapathysubramanian and Soumik Sarkar},
journal= {arXiv preprint arXiv:1811.06067},
year = {2022}
}
Comments
Workshop on Machine Learning for Molecules and Materials (MLMM), Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canada