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Photocatalysis has emerged as an effective tool for addressing the contemporary challenges in organic synthesis. However, the trial-and-error-based screening of feasible substrates and optimal reaction conditions remains time-consuming and potentially expensive in industrial practice. Here, we demonstrate an electrochemical-based data-acquisition approach that derives a simple set of redox-relevant electro-descriptors for effective mechanistic analysis and performance evaluation through machine learning (ML) in photocatalytic synthesis. These electro-descriptors correlate to the quantification of shifted charge transfer processes in response to the photoirradiation and enabled construction of reactivity diagram where high-yield reactive “hot zones” can reflect subtle changes of the reaction system. For the model reaction of photocatalytic deoxygenation reaction, the influence of varying carboxylic acids (substrate A, oxidation-intended) and alkenes (substrate B, reduction-intended) and varying reaction conditions on the reaction yield can be visualized, while mathematical analysis of the electro-descriptor patterns further revealed distinct mechanistic/kinetic impacts from different substrates and conditions. Additionally, in the application of ML algorithms, the experimentally derived electro-descriptors reflect an overall redox kinetic outcome contributed from vast reaction parameters, serving as a capable means to reduce the dimensionality in the case of complex multiparameter chemical space. As a result, utilization of electro-descriptors enabled efficient and robust quantitative evaluation of chemical reactivity, demonstrating promising potential of introducing operando-relevant experimental insights in the data-driven chemistry.

microRNAs (miRNAs) play critical regulatory roles in plant growth and development. In the present study, the function of herbaceous peony (Paeonia lactifloraPall.) miR156e-3p in the regulation of color formation has been investigated. Firstly, P. lactifloramiR156e-3p precursor sequence (pre-miR156e-3p) was isolated. Subsequently, the overexpression vector of pre-miR156e-3p was constructed and transformed into Arabidopsis thaliana. Moreover, the medium screening, GUS staining, polymerase chain reaction (PCR) of the GUS region and real-time quantitative PCR (qRT-PCR) of miR156e-3p all confirmed that the purpose gene had been successfully transferred into Arabidopsisplants and expressed, which resulted in apparent purple lateral branches. And this change in color was caused by the improved anthocyanin accumulation. In addition, expression analysis had shown that the level of miR156e-3p transcript was increased, while transcription level of target gene squamosa promoter binding protein-like gene (SPL1), encoding SPLtranscription factor that negatively regulated anthocyanin accumulation, was repressed in miR156e-3p-overexpressing transgenic plants, and its downstream gene dihydroflavonol 4-reductase gene (DFR) that was directly involved in anthocyanin biosynthesis was strongly expressed, which resulted in anthocyanin accumulation of Arabidopsislateral branches. These findings would improve the understanding of miRNAs regulation of color formation in P. lactiflora.