Modeling Multilevel Donor–Acceptor Interactions for Device‐Level Efficiency Prediction in Organic Solar Cells

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Chinese explanation / 中文解读

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Original abstract

Organic solar cells (OSCs) are promising low‐cost photovoltaic technologies, yet their further performance improvement remains constrained by the empirical and labor‐intensive optimization of donor–acceptor (D–A) material pairings. A key challenge lies in the fact that device‐level properties, such as power conversion efficiency (PCE), are governed by complex intermolecular interactions between donor and acceptor components rather than by their intrinsic properties in isolation. Here, we propose an interaction‐aware computational framework for modeling D–A compatibility in OSCs, aimed at device‐level efficiency prediction. By integrating atomic‐, motif‐, and molecular‐level representations within a unified graph‐based architecture, our framework captures cooperative structural and electronic effects across multiple chemical scales that are critical to OSC device performance. Unlike existing computational methods that treat donor and acceptor materials independently or rely on simple feature concatenation, our method is designed to learn cross‐molecular interactions relevant to organic photovoltaic devices. Evaluated on experimentally measured OSC datasets, our model consistently outperforms existing graph‐based approaches, achieving approximately a 20% reduction in mean absolute error for PCE prediction, highlighting the importance of modeling D–A interactions for accelerating OSC material screening and rational device optimization.

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