Overcoming Data Starvation: Automated Virtual Reaction Exploration and Machine Learning Discovery of p-Block Metal Catalysts for Borrowing Hydrogen.

(De)Hydrogenation processes, traditionally dominated by d-block transition metals, offer a sustainable route for molecular synthesis using alcohols as feedstocks. However, reliance on noble metals, mechanistic complexity, and limited substrate scope drive the search for alternatives. p-Block metals represent an attractive but long-standing challenge due to their strong oxophilicity and Lewis acidity. In particular, the lack of d-orbitals for electronic buffering impedes catalytic dehydrogenation/hydrogenation cycling and confines p-block hydrides to stoichiometric use. To overcome these intrinsic limitations and data starvation for AI-driven design, we present an intelligent framework that bypasses high-throughput experimentation (HTE) by integrating automated reaction pathway exploration with machine learning (ML). This approach enables de novo discovery of p-block catalysts in data-scarce regimes. We demonstrate its power by developing a homogeneous indium-based catalyst for borrowing hydrogen (BH)-mediated N-alkylation, featuring broad substrate scope, operational simplicity, and synthetic accessibility. The catalytically active indium-hydride (In-H) species was confirmed by in situ 1H NMR. This work not only establishes the first efficient p-block BH catalyst but also introduces a mechanism-informed, artificial intelligence (AI)-guided paradigm for main-group catalysis, expanding the frontiers of catalysis and sustainable synthesis.