用预训练图神经网络+生成强化学习，加速高效有机光伏材料发现
Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning
- 结合GNN预训练与GPT-2风格的强化学习生成分子
- 设计出预测效率接近21%的新分子结构
- 开源近3000对供体-受体数据集，助力社区研究
有机光伏（OPV）材料为低成本太阳能利用提供了前景。然而，优化供体-受体（D-A）组合以实现高光电转换效率（PCE）仍是重大挑战。本文提出一种框架，融合大规模图神经网络（GNN）预训练与基于GPT-2的强化学习（RL）策略，用于设计具有高潜力PCE的OPV分子。该方法生成的候选分子预测效率可达21%，但需进一步实验验证。此外，我们进行了初步片段级分析，识别出被RL模型识别出可能提升PCE的结构片段，为研究者提供设计指导。为促进持续发现，我们正构建迄今最大的开源OPV数据集，预计包含近3000对供体-受体组合。最后，我们计划与实验团队合作，合成并表征AI设计的分子，以获取新数据，持续优化预测与生成模型。
原文摘要 · Abstract (English)
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 (Generative Pretrained Transformer 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.
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