Initially, our platform was employed for reactivity screening to acquire labeled data points essential for training our machine learning models about substrate suitability for C(sp )-H oxidation. To ensure a diverse chemical space in our training dataset, using a similar approach reported in previous literature , we randomly selected 335 chemicals available in our laboratory and subjected them to predetermined electrochemical conditions to enable rapid generation of data points, allowing us to classify each substrate based on its reactivity (Section S2.2 of the Supporting Information). In particular, the reactions targeted the transformation of substrates into ketone or alcohol products, using mediator-catalyzed C(sp )-H bond oxidation, with the outcomes verified through NMR spectroscopy by monitoring the appearance of signature peaks indicative of these products. )-H oxidation process using the electrocatalyst. (B) Semantic literature analysis for reaction data mining using a language model with natural language prompts. Performance is evaluated by comparing the ground truth with LLMassigned labels and examining the impact of prompt quality. (C) Overview of training data preparation and machine learning models used for predicting electrochemical C-H oxidation reactivity (Task 1) and selectivity (Task 2). Models with different architectures are evaluated for accuracy and AUC.