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However, reliance on noble metals, mechanistic complexity, and limited substrate scope drive the search for alternatives.\n                    <jats:italic>p<\/jats:italic>\n                    \u2010Block metals represent an attractive but long\u2010standing challenge due to their strong oxophilicity and Lewis acidity. In particular, the lack of\n                    <jats:italic>d<\/jats:italic>\n                    \u2010orbitals for electronic buffering impedes catalytic dehydrogenation\/hydrogenation cycling and confines\n                    <jats:italic>p<\/jats:italic>\n                    \u2010block hydrides to stoichiometric use. To overcome these intrinsic limitations and data starvation for AI\u2010driven design, we present an intelligent framework that bypasses high\u2010throughput experimentation (HTE) by integrating automated reaction pathway exploration with machine learning (ML). This approach enables de novo discovery of\n                    <jats:italic>p<\/jats:italic>\n                    \u2010block catalysts in data\u2010scarce regimes. We demonstrate its power by developing a homogeneous indium\u2010based catalyst for borrowing hydrogen (BH)\u2010mediated\n                    <jats:italic>N<\/jats:italic>\n                    \u2010alkylation, featuring broad substrate scope, operational simplicity, and synthetic accessibility. The catalytically active indium\u2010hydride (In\u2013H) species was confirmed by in situ\n                    <jats:sup>1<\/jats:sup>\n                    H NMR. This work not only establishes the first efficient\n                    <jats:italic>p<\/jats:italic>\n                    \u2010block BH catalyst but also introduces a mechanism\u2010informed, artificial intelligence (AI)\u2010guided paradigm for main\u2010group catalysis, expanding the frontiers of catalysis and sustainable synthesis.\n                  <\/jats:p>","DOI":"10.1002\/anie.4351270","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T17:16:14Z","timestamp":1781198174000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Overcoming Data Starvation: Automated Virtual Reaction Exploration and Machine Learning Discovery of\n                    <i>p<\/i>\n                    \u2010Block Metal Catalysts for Borrowing Hydrogen"],"prefix":"10.1002","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7376-893X","authenticated-orcid":false,"given":"Zhe","family":"Chen","sequence":"first","affiliation":[{"name":"School of Materials Science and Engineering PCFM Lab the Key Laboratory of Low\u2010Carbon Chemistry &amp; Energy Conservation of Guangdong Province Sun Yat\u2010sen University  Guangzhou P. 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4113-3938","authenticated-orcid":false,"given":"Yubang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Materials Science and Engineering PCFM Lab the Key Laboratory of Low\u2010Carbon Chemistry &amp; Energy Conservation of Guangdong Province Sun Yat\u2010sen University  Guangzhou P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1684-918X","authenticated-orcid":false,"given":"Ruzhao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Chemistry Sun Yat\u2010sen University  Guangzhou P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Chemistry Sun Yat\u2010sen University  Guangzhou P. R. 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Su","sequence":"additional","affiliation":[{"name":"School of Materials Science and Engineering PCFM Lab the Key Laboratory of Low\u2010Carbon Chemistry &amp; Energy Conservation of Guangdong Province Sun Yat\u2010sen University  Guangzhou P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3472-3413","authenticated-orcid":false,"given":"Yinwu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Materials Science and Engineering PCFM Lab the Key Laboratory of Low\u2010Carbon Chemistry &amp; Energy Conservation of Guangdong Province Sun Yat\u2010sen University  Guangzhou P. R. 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