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Cited by 35CO2 Reduction Techniques and CatalystsMachine Learning in Materials ScienceElectrocatalysts for Energy Conversion

Evaluation of Machine Learning Models on Electrochemical CO2 Reduction Using Human Curated Datasets

Farris, Brianna R., Niang-Trost, Tevin, Branicky, Michael S., Leonard, Kevin C.·2022·ACS Sustainable Chemistry & Engineering
DOI 10.1021/acssuschemeng.2c02941
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Abstract

Machine learning holds the potential to be a powerful tool to aid in designing catalytic and sustainable chemical systems. However, it is important for experimental researchers to understand the capabilities of different machine learning models when trained on experimental data. In this work, we trained three different machine learning algorithms (decision tree, random forest, and multilayer perceptron) with a hand-curated dataset of 127 reaction conditions for electrocatalytic CO 2 reduction on heterogeneous catalysts in aqueous electrolytes. The input to the machine learning models were the experimental conditions, and we posed four separate outputs to each of these machine learning algorithms: (1) if the number of proton-coupled electron transfer events was greater than two, (2) if carbon–carbon coupling occurred, (3) if ethylene was the major product, and (4) major product prediction. We observed that with a dataset of this size, all three machine learning models could achieve accuracies between 0.7 and 0.8 for the three binary classification problems (1, 2, and 3). Also, the shallow learning decision tree and random forest models performed equal to or better than the deep learning multilayer perceptron models. In the multiclass classification problem (i.e., predicting the product) the accuracy for all models decreased, with the random forest model producing the highest accuracy of 0.6. Analysis of the models showed that machine learning can independently arrive at conclusions that are well-known in the literature, e.g., that Cu is an important catalyst for producing high-carbon content products, and discern more-complicated patterns, with respect to feature importance.

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  • The random forest classifier's performance is comparable to values reported in structure-activity prediction studies based on experimental data in electrocatalytic reactions.

    “This level of performance is comparable to values reported in structure–activity prediction studies based on experimental data in the electrocatalytic reactions [11,38], for example, a classification model predicting CO2 reduction reaction (CO2RR) indicators achieved similar results on over 20 test samples [38].”

    From Automating structure-activity analysis for electrochemical nitrogen reduction catalyst design through multi-agent collaborations (2025) · cites this paper as [38]

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Authors
Farris, B. R., Niang-Trost, T., Branicky, M. S., & Leonard, K. C.
Affiliations
University of Kansas (US)
Corresponding author
Leonard, Kevin C.
ORCID
Venue
ACS Sustainable Chemistry & Engineering
Publisher
American Chemical Society (ACS)
Published
2022-08-10
Volume
10
Issue
33
Pages
10934-10944
ISSN
2168-0485 (ISSN-L)
DOI
10.1021/acssuschemeng.2c02941
OpenAlex
W4292666425
Cited by
35
References
35
Citations by year
2026: 13 · 2025: 11 · 2024: 6 · 2023: 5
FWCI
0.99
Type
journal article
Language
en
Open access
closed
Licence
Locations
ACS Sustainable Chemistry & Engineering (publishedVersion)
Funders
Division of Graduate Education (DGE-1922649)
Topics
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › CO2 Reduction Techniques and Catalysts
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Electrocatalysts for Energy Conversion
Keywords
electrochemical CO2 reduction, electrocatalysis, heterogeneous catalysts, aqueous electrolyte solutions, classifier comparison, random forest, decision tree, multilayer perceptron, ethylene production, carbon-carbon coupling, proton-coupled electron transfer, copper catalysts, feature importance

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