Autonomous Materials Discovery for Organic Photovoltaics

Changhyun Hwang, Seungjoo Yi, David Friday, Nicholas Henry Angello, Tiara Charis Torres-Flores, Nick Jackson, Martin D. Burke, Charles Schroeder, Ying Diao

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