Autonomous Materials Discovery for Organic PhotovoltaicsDownload PDF

27 Sept 2022, 21:35 (modified: 29 Nov 2022, 19:11)AI4Mat 2022 PosterReaders: Everyone
Keywords: automated synthesis, automated material characterization, inverse material design, organic solar cells
TL;DR: Developing an AI-guided autonomous materials design platform that would be able to design molecules with desired properties, synthesize the molecules and fabricate organic solar cell devices.
Abstract: We aim to develop an AI-guided autonomous materials design approach to discover high-performance organic photovoltaics (OPVs). Autonomous synthesis, automated characterization, and AI-based methods will be integrated into a closed-loop approach to drive molecular discovery guided by target criteria for OPV performance: efficiency and stability. The long-term goal of the project is two-fold: (1)in terms of fundamental science, we aim to fill key knowledge gaps in understanding how molecular structure determines OPV stability and efficiency, and advance the science of closed-loop autonomous discovery by learning how to synergistically integrate AI, automated synthesis, and automated testing. (2)In terms of technology, we aim to meet the “10-10” target (10\% efficiency and 10-year stability for OPV materials) to make OPVs a commercial reality for next-generation energy capture applications and for mitigating climate change.
Paper Track: Proposals
Submission Category: AI-Guided Design, Automated Chemical Synthesis, Automated Material Characterization
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