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We present an automated data-collection pipeline involving a convolutionalneural network and a large language model to extract user-specified tabulardata from peer-reviewed literature. The pipeline is applied to 74 reportspublished between 1957 and 2014 with experimentally-measured oxidationpotentials for 592 organic molecules (-0.75 to 3.58 V). After data curation(solvents, reference electrodes, and missed data points), we trained multiplesupervised machine learning models reaching prediction errors similar toexperimental uncertainty ($\sim$0.2 V). For experimental measurements ofidentical molecules reported in multiple studies, we identified the most likelyvalue based on out-of-sample machine learning predictions. Using the trainedmachine learning models, we then estimated oxidation potentials of $\sim$132ksmall organic molecules from the QM9 data set, with predicted values spanning0.21 to 3.46 V. Analysis of the QM9 predictions in terms of plausibledescriptor-property trends suggests that aliphaticity increases the oxidationpotential of an organic molecule on average from $\sim$1.5 V to $\sim$2 V,while an increase in number of heavy atoms lowers it systematically. Thepipeline introduced offers significant reductions in human labor otherwiserequired for conventional manual data collection of experimental results, andexemplifies how to accelerate scientific research through automation.

Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials