Data · dataset · 2026
High-Throughput Screening of MN<sub>4</sub> Coordinated Macrocyclic Frameworks for CO Reduction by Combining First-Principles Calculations and Machine Learning
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The electrochemical CO reduction reaction (CORR) by single-atom catalysts (SACs) provides a promising route for converting CO into value-added C<sub>1</sub> products, but the rational discovery of efficient SACs remains challenging because of the large configurational space and complex reaction network.
Description
Herein, we chose two-dimensional periodic macrocyclic frameworks with MN<sub>4</sub> coordination centers as the SACs and established a multilevel screening strategy by combining first-principles calculations and machine learning (ML).
After evaluating thermodynamic stability, dissolution resistance, *CO adsorption, and hydrogen evolution reaction suppression, 90 candidates were screened out from 156 SACs for further analysis. The hydrogenation of *CO to *CHO was identified as a key step for deep CO reduction, and ΔG<sub>(*CHO‑*CO)</sub> was selected as the main descriptor. An ExtraTrees regression model trained on the screened SACs achieved high predictive accuracy, with R<sub>2</sub> values of 0.947 and 0.925 for the training and test sets, respectively.
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By scanning the full reaction pathway of CORR (CH<sub>4</sub> as the final product) on the SACs with relatively low ΔG<sub>(*CHO‑*CO)</sub> value, the FeC<sub>8</sub>N<sub>8</sub> was found to exhibit the highest catalytic activity among the examined candidates, with a low limiting potential (U<sub>L</sub>) of −0.30 V. To accelerate catalyst evaluation, ΔG<sub>(*CHO‑*CO)</sub>, which describes the first hydrogenation of adsorbed *CO, was selected as the key descriptor and predicted by ML algorithm.
Full-pathway calculations were subsequently carried out for catalysts with relatively low ΔG<sub>(*CHO‑*CO)</sub> values to validate the descriptor-guided screening results.
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Where it is published
- DOI doi.org/10.1021/acs.jpcc.6c05023.s001 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Chemistry · Computer Science & AI · Earth & Environmental Science · Life Sciences · Machine learning
- Inferred from text
- Density functional theory 65%
Provenance · 1 source records, 11 field assertions
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|---|---|---|---|
| ZivaHub | oai:figshare.com:article/34018269 | 10 d ago | JSON v1 |
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| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |