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The paper introduces CycleChemist, a dual-pronged machine learning framework for organic photovoltaic (OPV) material discovery, addressing the limitation of existing methods that focus on either donor or acceptor materials in isolation. They curate the Organic Photovoltaic Donor Acceptor Dataset (OPV2D), containing 2000 experimentally characterized donor-acceptor pairs, and develop a hierarchical graph neural network (OPVC) to predict OPV behavior, incorporating multi-task learning and donor-acceptor interaction modeling. The framework also includes MatGPT, a generative transformer for producing synthetically accessible organic semiconductors, guided by reinforcement learning to optimize material properties.
A new dual-ML framework, CycleChemist, accelerates organic photovoltaic discovery by jointly modeling donor-acceptor interactions and generating synthesizable candidates with reinforcement learning.
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.
Tsinghua AI