Organic optoelectronic materials are a promising avenue for next-generation electronic devices due to their solution processability, mechanical flexibility, and tunable electronic properties. In particular, near-infrared (NIR) sensitive molecules have unique applications in night-vision equipment and biomedical imaging. Molecular engineering has played a crucial role in developing non-fullerene acceptors (NFAs) such as the Y-series molecules, which have significantly improved the power conversion efficiency (PCE) of solar cells and enhanced spectral coverage in the NIR region. However, systematically designing molecules with targeted optoelectronic properties while ensuring synthetic accessibility remains a challenge. To address this, we leverage structural priors from domain-focused, patent-mined datasets of organic electronic molecules using a symmetry-aware fragment decomposition algorithm and a fragment-constrained Monte Carlo Tree Search (MCTS) generator. Our approach generates candidates that retain symmetry constraints from the patent dataset, while also exhibiting red-shifted absorption, as validated by TD-DFT calculations.

翻译：有机光电材料因其溶液可加工性、机械柔性和可调控的电子特性，成为下一代电子器件极具前景的发展方向。其中，近红外敏感分子在夜视设备和生物医学成像领域具有独特的应用价值。分子工程在开发非富勒烯受体方面发挥了关键作用，例如Y系列分子显著提升了太阳能电池的功率转换效率，并拓展了近红外区域的光谱覆盖范围。然而，如何在确保合成可行性的同时，系统性地设计具有目标光电特性的分子仍是一个挑战。为此，我们利用领域聚焦的专利挖掘有机电子分子数据集中的结构先验，通过对称感知的片段分解算法和片段约束的蒙特卡洛树搜索生成器，构建了分子生成方法。该方法生成的候选分子既保持了专利数据集中的对称性约束，又表现出红移吸收特性，这一结果已通过TD-DFT计算验证。