Machine learning prediction of small molecule passivators and their impacts on the passivation and photocatalytic performance of organic-inorganic hybrid perovskite interfaces

Abstract

Organic-inorganic hybrid perovskite materials show great potential in photocatalysis and solar cells due to their excellent photoelectric properties, while interface defects affect their photocatalytic performance and stability. In this study, machine learning techniques were used to perform preliminary screening and prediction of high-performance passivation molecules (PMs), and density functional theory was used to investigate the effect of PMs on interfacial passivation performance. It was found that the presence of different chemical bonds between PMs and the interface can significantly change the interface properties. Therefore, the effect of PMs on the performance of interfacial photocatalytic CO₂ reduction reaction was explored. When PMs present N-Pb bonds at the interface, CO₂ is reduced to CH₃OH, while S-Pb bonds selectively generate CH₂O from CO₂, making perovskite selectively generate O-containing carbonyl compounds. The autocatalytic performance of organic compounds at the perovskite interface is poor and is not easy to occur. This study combines perovskite interface passivation and photocatalytic performance, providing a new approach for selective catalysis at perovskite interfaces.

Keywords

Organic-inorganic hybrid perovskite / functional ligand organic small molecules / interface passivation / photocatalytic CO₂RR / machine learning

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