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

Jiangjie Qiu, Hou Hei Lam, Xiuyuan Hu, Wentao Li, Siwei Fu, Fankun Zeng, Hao Zhang, Xiaonan Wang

Submission Track: Paper Track (Tiny Paper)

Submission Category: AI-Guided Design

Keywords: Generative Reinforcement Learning, GPT-2, Power Conversion Efficiency, Molecular Design, Organic Photovoltaics, Graph Neural Networks

TL;DR: We propose an end-to-end approach that combines pretrained graph neural networks with a GPT-2-based reinforcement learning generator for the automated design and accurate PCE prediction of efficient organic photovoltaic donor-acceptor pairs.

Abstract: Organic photovoltaic (OPV) materials offer a promising avenue toward cost-effective solar energy utilization. However, optimizing donor-acceptor (D-A) combinations to achieve high power conversion efficiency (PCE) remains a significant challenge. In this work, we propose a framework that integrates large-scale pretraining of graph neural networks (GNNs) with a GPT-2-based reinforcement learning (RL) strategy to design OPV molecules with potentially high PCE. This approach produces candidate molecules with predicted efficiencies approaching 21\%, although further experimental validation is required. Moreover, we conducted a preliminary fragment-level analysis to identify structural motifs recognized by the RL model that may contribute to enhanced PCE, thus providing design guidelines for the broader research community. To facilitate continued discovery, we are building the largest open-source OPV dataset to date, expected to include nearly 3,000 donor-acceptor pairs. Finally, we discuss plans to collaborate with experimental teams on synthesizing and characterizing AI-designed molecules, which will provide new data to refine and improve our predictive and generative models.

Submission Number: 16