Pathways towards Boosting Solar-Driven Hydrogen Evolution of Conjugated Polymers

Photocatalytic H₂ evolution under solar illumination has been considered to be a promising technology for green energy resources. Developing highly efficient photocatalysts for photocatalytic water splitting is long-term desired but still challenging. Conjugated polymers (CPs) have attracted ongoing attention and have been considered to be promising alternatives for solar-driven H₂ production due to the excellent merits of the large π-conjugated system, versatile structures, tunable photoelectric properties, and well-defined chemical composites. The excellent merits have offered numerous methods for boosting photocatalytic hydrogen evolution (PHE) of initial CP-based photocatalysts, whose apparent quantum yield is dramatically increased from <1 to >20% in recent five years. According to the photocatalytic mechanism, this review herein systematically summarizes three major strategies for boosting photocatalytic H₂ production of CPs: 1) enhancing visible light absorption, 2) suppressing recombination of electron-hole pairs, and 3) boosting surface catalytic reaction, mainly involving eleven methods, that is, copolymerization, modifying cross-linker, constructing a donor-acceptor structure, functionalization, fabricating organic heterojunction, loading cocatalyst, and surface modification. Finally, the perspectives towards the future development of PHE are proposed.

Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors

Hydrogen evolution by alternating conjugated copolymers has attracted much attention in recent years. To study alternating copolymers with data-driven strategies, two types of multidimension fragmentation descriptors (MDFD), structure-based MDFD (SMDFD), and electronic property-based MDFD (EPMDFD), have been developed with machine learning (ML) algorithms for the first time. The superiority of SMDFD-based models has been demonstrated by the highly accurate and universal predictions of electronic properties. Moreover, EPMDFD-based, experimental-parameter-free ML models were developed for the prediction of the hydrogen evolution reaction, displaying excellent accuracy (real-test accuracy = 0.91). The combination of explainable ML approaches and first-principles calculations was employed to explore photocatalytic dynamics, revealing the importance of electron delocalization in the excited state. Virtual designing of high-performance candidates can also be achieved. Our work illustrates the huge potential of ML-based material design in the field of polymeric photocatalysts toward high-performance photocatalysis.