Workshop: AI for Accelerated Materials Design (AI4Mat)

Autonomous Materials Discovery for Organic Photovoltaics

Changhyun Hwang · Seungjoo Yi · David Friday · Nicholas Angello · Tiara Torres-Flores · Nick Jackson · Martin Burke · Charles Schroeder · Ying Diao

Keywords: [ organic solar cells ] [ automated synthesis ] [ automated material characterization ] [ inverse material design ]


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.

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