Conjugated polyelectrolytes (CPEs) are versatile organic materials with diverse applications, but their myriad molecular structures hinder traditional materials discovery. This study uses a data-centric approach, combining machine learning with high-throughput first-principles calculations, to systematically examine how key properties depend on CPE structural components. Structure-property relationships are established, crucial structural features are uncovered, and these features are used as descriptors to predict properties of unknown CPEs. Promising CPEs are discovered for use as hole transport materials in halide perovskite-based optoelectronic devices and as photocatalysts for water splitting.

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