Polymers are appealing candidates for photoelectric applications because of their intrinsic characteristics include flexibility in substrate compatibility, ease of manufacturing, room temperature operating conditions, and adaptable optoelectronic properties. In present study, we have used the machine learning (ML) for property prediction and polymer designing. Multiple ML models are trained. 10,000 novel polymers are generated using automatic method. The synthetic accessibility of the chosen polymers is predicted. Structural similarity among the selected polymers is also calculated that indicated good structural similarity among the chosen polymers. By effectively identifying and optimizing novel polymers, the implemented methods substantially increase the chances of uncovering superior materials suited for advanced applications.

中文翻译：

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彻底改变光电探测器的聚合物工程：快速材料发现的机器学习辅助范例

聚合物是光电应用中颇具吸引力的候选者，因为它们的固有特性包括基板兼容性的灵活性、易于制造、室温操作条件和适应性强的光电特性。在本研究中，我们使用机器学习（ML）进行性能预测和聚合物设计。训练多个 ML 模型。使用自动方法生成 10,000 种新型聚合物。预测了所选聚合物的合成可及性。还计算了所选聚合物之间的结构相似性，表明所选聚合物之间具有良好的结构相似性。通过有效识别和优化新型聚合物，所实施的方法大大增加了发现适合先进应用的优质材料的机会。