In this study, an intricate machine learning assisted framework is introduced for the designing of polymer acceptors. Machine learning (ML) models are trained to predict the electron affinity of polymers. Ten thousand new polymers are generated using the Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) method and their electron affinity values are predicted using a pre-trained ML model. The selection of polymers is based on electron affinity, retaining those with higher electron affinity. The synthetic accessibility of the selected polymers is evaluated. Additionally, the structural similarity among the selected polymers is examined; results are indicating higher similarity between selected compounds. By efficiently identifying and optimizing new polymers, the approaches developed significantly enhance the likelihood of discovering superior materials for advanced applications.

中文翻译：

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具有更高电子亲和力的有机太阳能电池聚合物受体的化学库生成

在这项研究中，引入了复杂的机器学习辅助框架来设计聚合物受体。机器学习 (ML) 模型经过训练可以预测聚合物的电子亲和力。使用打破逆合成有趣的化学子结构 (BRICS) 方法生成一万种新聚合物，并使用预先训练的 ML 模型预测它们的电子亲和力值。聚合物的选择基于电子亲和力，保留具有较高电子亲和力的聚合物。评估所选聚合物的合成可及性。此外，还检查了所选聚合物之间的结构相似性；结果表明所选化合物之间具有更高的相似性。通过有效识别和优化新聚合物，所开发的方法显着提高了发现用于先进应用的优质材料的可能性。