Data driven discovery of conjugated polyelectrolytes for optoelectronic and photocatalytic applications

作     者：Yangyang Wan Fernando Ramirez Xu Zhang Thuc-Quyen Nguyen Guillermo C.Bazan Gang Lu 

作者机构：Department of Physics and AstronomyCalifornia State University NorthridgeNorthridgeCaliforniaUSA Department of Chemistry and BiochemistryUniversity of CaliforniaSanta BarbaraCaliforniaUSA 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2021年第7卷第1期

页      面：641-649页

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 070304[理学-物理化学(含∶化学物理)] 08[工学] 0817[工学-化学工程与技术] 0703[理学-化学] 

基　　金：The work was supported by the U.S.National Science Foundation(DMREF-1922042) 

主　　题：conjugated backbone photocatalytic 

摘      要：Conjugated polyelectrolytes(CPEs),comprised of conjugated backbones and pendant ionic functionalities,are versatile organic materials with diverse ***,the myriad of possible molecular structures of CPEs render traditional,trial-and-error materials discovery strategy ***,we tackle this problem using a data-centric approach by incorporating machine learning with high-throughput first-principles *** systematically examine how key materials properties depend on individual structural components of CPEs and from which the structure–property relationships are *** 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 ***,we discover promising CPEs as hole transport materials in halide perovskite-based optoelectronic devices and as photocatalysts for water *** work could accelerate the discovery of CPEs for optoelectronic and photocatalytic applications.