Automated DFT-Machine Learning Integration Enables Data-Efficient and Generalizable Feasibility Predictions in Metallaphotoredox sp2–sp3 Cross-Coupling Reactions https://doi.org/10.1021/acscatal.5c07857 Nickel/photoredox catalysis in cross-coupling reactions offers mild operating conditions for efficient C–C bond formation, expanding synthetic access to pharmaceutically relevant molecules. However, routine implementation of such reactions remains constrained by the intricate reaction mechanism and limited availability of experimental data, which complicate optimization tasks and the development of predictive models. The integration of quantum-mechanical (QM) calculations with machine learning (ML) has proven to be effective for developing predictive models of complex reactions with sparse experimental data. Here, we present a combined approach that integrates automated density functional theory calculations, ML, and parallel synthesis to develop quantum mechanics-machine learning (QM-ML) models for the nickel metallophotoredox cross-coupling reaction feasibility prediction. Random-Forest classification models are trained to predict the outcome of a given reaction using DFT-computed descriptors from automatically generated 3D structures of catalytic cycle intermediates. We demonstrate the broad applicability of this approach, applying it to a diverse data set encompassing four reaction subtypes, namely, bromide cross-electrophile couplings, chloride cross-electrophile couplings, deoxygenative couplings, and amino radical transfer (ART) couplings, augmented with additional experiments curated by a systematic cheminformatics method to broaden the alkyl halides scope. We show on a blind literature data set that such a QM-ML approach can successfully predict the feasibility of complex reactions from heterogeneous data sets with minimal data requirements and can generalize it to unseen reaction subtypes with a few-shot learning approach, affording a computational model for ART coupling. Together, these capabilities provide a data-efficient solution for rapidly predicting the outcome of cross-coupling reactions and facilitate the adoption of nickel photocatalysis in the MAKE stage of the DMTA cycle. 📕ACS Catalysis (IF=13.1) #method