Context-Aware Learning for Inverse Design in Photovoltaics

Technical Report · 10 May 2022

DOI:https://doi.org/10.2172/2371715· OSTI ID:2371715

Ganapathysubramanian, Baskar ^[1]; Sarkar, Soumik ^[2]; Hegde, Chinmay ^[3]; Larsen, Ross ^[4]; Krishnamurthy, Adarsh ^[2]; Bao, Zhenan ^[5]; Larson, Bryon ^[4]

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1. Iowa State Univ., Ames, IA (United States); Iowa State University
2. Iowa State Univ., Ames, IA (United States)
3. New York Univ. (NYU), NY (United States)
4. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
5. Stanford Univ., CA (United States)

This document describes progress in the ARPA-E DIFFERENTIATE project titled “Context-Aware Learning for Inverse Design in Photovoltaics” during the period of May 2019 to May 2022. This project is being performed at Iowa State University, New York University, Stanford University, and National Renewable Energy Laboratory. The project aims to develop a new machine learning (ML) framework to significantly accelerate the design of organic microstructures for improved organic photovoltaic performance. In this project, we had developed an inverse design framework using Deep Learning called InvNets for generating microstructures with desired physics-driven properties. As a preliminary product, in Milestone 3, we demonstrated how InvNets show 20% improvement in the performance of the microstructures and over 100X speedup in the performance compared to traditional processes for physics-driven inverse design. Later, in Milestone 6, we demonstrated that InvNets work for more complex physics properties, specifically, generating microstructures for organic photovoltaic cells with desired current-voltage characteristics. Further, in Milestone 4, we explored the idea of using physics-aware surrogates for obtaining solutions of partial differential equations(PDE) called as DiffNets(now called as NeuFENets to avoid ambiguity of names). The connection between both frameworks is that DiffNet surrogates form the physics-aware surrogate in the InvNet framework. Finally in Milestone 8, we extend our framework for other physics domains. Specifically, we explore building geometry-aware NeuFENets by developing physics surrogates that exploit ideas from traditional immersed boundary finite element methods. With these updates, we are able to achieve all the Milestones.

• Research Organization: Iowa State Univ., Ames, IA (United States)
• Sponsoring Organization: USDOE Advanced Research Projects Agency - Energy (ARPA-E)
• DOE Contract Number: AR0001215
• OSTI ID: 2371715
• Report Number(s): DOE-ISU--01215-1
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
deep learning
generative AI
machine learning
organic photovoltaics
organic solar cells