Interpretable deep learning for guided structure-property explorations in photovoltaics

Autor:	Pokuri, Balaji Sesha Sarath, Ghosal, Sambuddha, Kokate, Apurva, Ganapathysubramanian, Baskar, Sarkar, Soumik
Rok vydání:	2018
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Zdroj:	npj Comput Mater 5, 95 (2019)
Druh dokumentu:	Working Paper
DOI:	10.1038/s41524-019-0231-y
Popis:	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. Comment: Workshop on Machine Learning for Molecules and Materials (MLMM), Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canada
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.06067 Zobrazit plný text záznamu View this record from Arxiv