<ul><li>A new framework combining graph neural networks and reinforcement learning has been proposed to design high-efficiency organic photovoltaic (OPV) molecules.</li><li>The integrated approach includes large-scale pretraining of graph neural networks and a Generative Pretrained Transformer 2 (GPT-2) based reinforcement learning strategy.</li><li>The proposed approach has predicted efficiencies approaching 21%, and provides design guidelines for enhancing power conversion efficiency (PCE).</li><li>To support further discovery, the largest open-source OPV dataset is being built, and collaboration with experimental teams is planned for synthesizing and characterizing AI-designed molecules.</li></ul>

Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning

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