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The problem has proven challenging to both classical computational\u2010chemistry approaches and to modern Machine Learning (ML) methods, the latter suffering from the combination of data scarcity (including all\u2010important negative examples) and the inaccuracy of chemoinformatic vectorization schemes. The current work addresses both problems, deploying affordable chemical robotics to curate a structurally diverse set of both active and inactive substrates, and then using this data to develop a physics\u2010grounded ML model for substrate scope prediction. This model uses only a handful of features to learn the proper balance between steric and electronic factors even from small datasets. 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