Data driven discovery of conjugated polyelectrolytes for optoelectronic and photocatalytic applications
Data driven discovery of conjugated polyelectrolytes for optoelectronic and photocatalytic applications
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DOI：
10.1038/s41524-021-00541-5
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发表时间：
2021-05
影响因子：
9.7
通讯作者：
Yangyang Wan;F. Ramírez;Xu Zhang;Thuc‐Quyen Nguyen;G. Bazan;G. Lu
中科院分区：
文献类型：
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作者：
Yangyang Wan;F. Ramírez;Xu Zhang;Thuc‐Quyen Nguyen;G. Bazan;G. Lu
共轭聚电解质（CPE）是一类具有共轭主链和离子侧基的多功能有机材料，具有广泛的应用前景。然而，CPE的无数可能的分子结构使得传统的试错材料发现策略不切实际。在这里，我们使用以数据为中心的方法，通过将机器学习与高吞吐量的第一原理计算相结合来解决这个问题。我们系统地研究了关键材料的性能如何依赖于CPE的各个结构组成部分，并从其中建立了结构-性能关系。通过机器学习，我们发现的CPE的性质至关重要的结构特征，这些功能，然后使用机器学习中的描述符来预测未知CPE的属性。最后，我们发现有前途的CPE作为空穴传输材料的卤化物钙钛矿基光电器件和作为光催化剂的水裂解。我们的工作可以加速发现光电和光催化应用的CPE。
Conjugated polyelectrolytes (CPEs), comprised of conjugated backbones and pendant ionic functionalities, are versatile organic materials with diverse applications. However, the myriad of possible molecular structures of CPEs render traditional, trial-and-error materials discovery strategy impractical. Here, we tackle this problem using a data-centric approach by incorporating machine learning with high-throughput first-principles calculations. We systematically examine how key materials properties depend on individual structural components of CPEs and from which the structure–property relationships are established. By means of machine learning, we uncover structural features crucial to the CPE properties, and these features are then used as descriptors in the machine learning to predict the properties of unknown CPEs. Lastly, we discover promising CPEs as hole transport materials in halide perovskite-based optoelectronic devices and as photocatalysts for water splitting. Our work could accelerate the discovery of CPEs for optoelectronic and photocatalytic applications.