Publication date: Feb 23, 2023
We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict Faradaic Efficiencies for 11 CO2RR products, as a function of the applied voltage at which the reaction takes place, and the relative amounts of non equivalent surface sites, distinguished according to their nominal coordination. A satisfactory model accuracy is found only when discriminating data according to their provenance. On one hand, this result points at a qualitative agreement across reported experimental CO2RR trends for single-crystal surfaces with well-defined terminations. On the other, this finding hints at the presence of differences in nominally identical catalysts and/or CO2RR measurements, which result in quantitative disagreement between experiments.
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|6.5 MiB||data and notebooks updated|
|2023.28 (version v2) [This version]||Feb 23, 2023||DOI10.24435/materialscloud:nd-50|
|2022.121 (version v1)||Sep 26, 2022||DOI10.24435/materialscloud:44-pc|