2026-04-16T20:43:21Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:1658 2023-02-23T14:39:40Z openaire_data community-mcarchive

Rossi, Kevin Rossi, Kevin 2023-02-23 We mine from the literature experimental data on the CO<sub>2</sub> 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 CO<sub>2</sub>RR 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 CO<sub>2</sub>RR 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 CO<sub>2</sub>RR measurements, which result in quantitative disagreement between experiments. text/markdown text/plain application/x-xz https://doi.org/10.24435/materialscloud:nd-50 oai:materialscloud.org:1658 mcid:2023.28 eng Materials Cloud https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:sv-ak info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode catalysis co2 cu gaussian process What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO₂ reduction? A data-driven retrospective info:eu-repo/semantics/other