Campus Units

Computer Science, Electrical and Computer Engineering, Mechanical Engineering, Plant Sciences Institute

Document Type

Article

Publication Version

Published Version

Publication Date

10-1-2019

Journal or Book Title

npj Computational Materials

Volume

5

First Page

95

DOI

10.1038/s41524-019-0231-y

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.

Comments

This article is published as Pokuri, Balaji Sesha Sarath, Sambuddha Ghosal, Apurva Kokate, Soumik Sarkar, and Baskar Ganapathysubramanian. "Interpretable deep learning for guided microstructure-property explorations in photovoltaics." npj Computational Materials 5 (2019): 95. DOI: 10.1038/s41524-019-0231-y. Posted with permission.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

Springer Nature

Language

en

File Format

application/pdf

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