Abstract
Organic photovoltaic (OPV) materials offer a promising path toward sustainable energy generation, but their development is limited by the difficulty of identifying high performance donor and acceptor pairs with strong power conversion efficiencies (PCEs). Existing design strategies typically focus on either the donor or the acceptor alone, rather than using a unified approach capable of modeling both components. In this work, we introduce a dual machine learning framework for OPV discovery that combines predictive modeling with generative molecular design. We present the Organic Photovoltaic Donor Acceptor Dataset (OPV2D), the largest curated dataset of its kind, containing 2000 experimentally characterized donor acceptor pairs. Using this dataset, we develop the Organic Photovoltaic Classifier (OPVC) to predict whether a material exhibits OPV behavior, and a hierarchical graph neural network that incorporates multi task learning and donor acceptor interaction modeling. This framework includes the Molecular Orbital Energy Estimator (MOE2) for predicting HOMO and LUMO energy levels, and the Photovoltaic Performance Predictor (P3) for estimating PCE. In addition, we introduce the Material Generative Pretrained Transformer (MatGPT) to produce synthetically accessible organic semiconductors, guided by a reinforcement learning strategy with three objective policy optimization. By linking molecular representation learning with performance prediction, our framework advances data driven discovery of high performance OPV materials.
Abstract (translated)
有机光伏（OPV）材料为可持续能源生成提供了一条有前景的道路，但其开发受限于难以识别具有高效功率转换效率（PCE）的高性能供体和受体对。现有的设计策略通常仅集中于供体或受体中的一个方面，而不是采用一种能够同时建模两者的方法。在这项工作中，我们引入了一个结合预测建模与生成分子设计的双机器学习框架来发现有机光伏材料。我们介绍了“有机光伏供体受体数据集”（OPV2D），这是同类中最大的精心策划的数据集之一，包含2000对经过实验验证的供体和受体配对。利用这个数据集，我们开发了有机光伏分类器（OPVC）来预测材料是否表现出有机光伏特性，并构建了一个层次化图神经网络，该网络结合了多任务学习及供体-受体相互作用建模功能。此框架包括分子轨道能量估计器（MOE2），用于预测最高占据分子轨道（HOMO）和最低未占分子轨道（LUMO）的能量水平，以及光伏性能预测器（P3），用于估算功率转换效率（PCE）。此外，我们引入了材料生成预训练变压器（MatGPT），通过结合一种具有三个目标策略优化的强化学习方法来生产可合成的有机半导体。通过将分子表示学习与性能预测相连接，我们的框架推进了高性能OPV材料的数据驱动发现过程。
URL
https://arxiv.org/abs/2511.19500
