The convergence of artificial intelligence and machine learning with material science holds significant promise in rapidly accelerating the development timelines of new high-performance polymeric materials. Within this context, we report an inverse design strategy for polycarbonate and polyester discovery based on a recommendation system that proposes polymerization experiments that are likely to produce materials with targeted properties or characteristics. Following the recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity for polyesters or polycarbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class.

中文翻译：

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聚碳酸酯和聚酯逆设计的推荐系统

人工智能和机器学习与材料科学的融合为快速加快新型高性能聚合物材料的开发进度提供了广阔的前景。在此背景下，我们基于推荐系统报告了聚碳酸酯和聚酯发现的逆向设计策略，该系统提出了可能产生具有目标性质或特性的材料的聚合实验。遵循由历史开环聚合结果驱动的系统的建议，我们针对聚酯或聚碳酸酯的单体转化率和分散性的特定范围进行了实验。实验结果与推荐目标非常吻合，每个类别获得的假阴性或阳性很少。