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Electrochemistry of surface‐bound molecules is of high importance for numerous electronic and sensor applications. Extracting the electron transfer rate is beneficial for understanding surface‐bound processes, but it requires experimental or computational rigor. We evaluate methods to determine electron transfer rates from large voltammetry sets from experiments via machine learning using decision tree ensembles, neural networks, and gaussian process regression models. We applied these to reproduce computational measures of electron transfer rates modeled by first principles. The best machine learning models were a random forest with 80 decision trees and a neural network with Bayesian regularization, producing root mean squared errors of 0.37 and 0.49 s−1, respectively, corresponding to mean percent errors of 0.38 % and 0.52 %, respectively. This work establishes machine learning methods for rapidly acquiring electron transfer rates across large datasets for widespread applications.

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The cover feature image shows a collage of a DNA strand, voltammetric curve, and machine learning code to represent the methods used in this work. Extraction of electron transfer rates from experimental voltammetry datasets tends to be cumbersome. This work utilizes machine learning as an artificial intelligence method to rapidly acquire rates from raw measurements of surface electrochemistry. Optimal methods from among random forests, neural networks, and Gaussian process regression approaches yield electron transfer rate values within 0.5 % of the estimates from first‐principles modeling. More information can be found in the Research Article by J. D. Slinker and co‐workers.