TheMeCat: Dataset of Thermocatalytic Conversion of CO2 to Methanol

Data-driven materials discovery to accelerate the development of new catalysts for the green transition shows great promise, but requires machine-interpretable experimental data. For this purpose, we compiled the TheMeCat dataset for the thermocatalytic conversion of CO<sub>2</sub> to methanol. TheMeCat is curated from experimental literature sources, and designed for hybrid computational and experimental catalyst workflows. The dataset captures key catalyst features, process parameters, and methanol conversion efficiencies. Upon detailed analysis of the generated dataset and the associated variability in reported values, we perform targeted error analysis to assess consistency and reinforce the need for clear, structured reporting.

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Publication Details

Journal
Scientific Data
Published
2026-01-03
DOI
https://doi.org/10.1038/s41597-025-06482-8
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

TheMeCat: Dataset of Thermocatalytic Conversion of CO2 to Methanol

Árpád I. Toldy, Annukka Santasalo-Aarnio, Ondřej Krejčí, Prajwal Pisal et al.
Scientific Data
Machine Learning in Materials Science
article

TheMeCat: Dataset of Thermocatalytic Conversion of CO2 to Methanol

Árpád I. Toldy, Annukka Santasalo-Aarnio, Ondřej Krejčí, Prajwal Pisal, Patrick Rinke
article en

Abstract

Data-driven materials discovery to accelerate the development of new catalysts for the green transition shows great promise, but requires machine-interpretable experimental data. For this purpose, we compiled the TheMeCat dataset for the thermocatalytic conversion of CO<sub>2</sub> to methanol. TheMeCat is curated from experimental literature sources, and designed for hybrid computational and experimental catalyst workflows. The dataset captures key catalyst features, process parameters, and methanol conversion efficiencies. Upon detailed analysis of the generated dataset and the associated variability in reported values, we perform targeted error analysis to assess consistency and reinforce the need for clear, structured reporting.

Scientific DataVol. 13(1)
BMW (Germany) (DE), University of Turku (FI), Technical University of Munich (DE), Aalto University (FI)
European Commission
Openalex Percentile: Top 98%
Machine Learning in Materials Science
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TheMeCat: Dataset of Thermocatalytic Conversion of CO2 to Methanol — Árpád I. Toldy, Annukka Santasalo-Aarnio, et al. · Scientific Data (2026) | TGRS Research Map | TGRS