Interpretable deep learning for guided microstructure-property explorations in photovoltaics
Abstract
The microstructure determines the photovoltaic performance of a thin film organic semiconductor film. The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate, thus making microstructure optimization challenging. Here, we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks. We characterize this approach in terms of two critical metrics, its generalizability (has it learnt a reasonable map?), and its intepretability (can it produce meaningful microstructure characteristics that influence its prediction?). A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration. We illustrate this by using the surrogate model for both manual exploration (that verifies known domain insight) as well as automated microstructure optimization. We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 01, 2019
- Source ID
- 10.1038/s41524-019-0231-y
Entities
People
- Apurva Kokate
- Balaji Sesha Sarath Pokuri
- Baskar Ganapathysubramanian
- Sambuddha Ghosal
- Soumik Sarkar
Organizations
- United States Air Force
- United States Department of Defense