Published June 3, 2025
| Version v3
Dataset
Open
Final geometries and energies, statistical analysis and estimated errors of single metals and bimetallics for CO2 to methanol conversion
Authors/Creators
Description
The dataset accommodate all the extra data discussed in:
Pisal, P., Krejčí, O. & Rinke, P. Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion. npj Comput Mater 11, 213 (2025). https://doi.org/10.1038/s41524-025-01664-9
The datased contains four types of data:
- All the final geometries and energies of adsorbated (*H, *O, *OCHO & *OCH3) and all the 158 single metals and bimetallic alloys on all the surfaces with Miller indices in {-2, -1, ... 2} optimized with Open Catalyst Project (OCP) 20 equiformer_V2 machine-learned force-field model. These are in the geometries_and_energies.zip file organized by the metal/alloys name, with the final geometries and enerigies in a json file, using a json ASE format.
- All the estimated mean absolute errors (MAE) of predicted adsorption energies for all the considered metals and bimetallic alloys in Estimated_MAEs_metals_bimetallics.csv and xlsx file. The data content is identical, files differs only by a format.
- All the adsorption energy disctibutions (AEDs) for all the 158 metals/alloys and adsorbates in AEDs_metals_bimetallics.csv and xlsx files. The data content is identical, files differs only by a format.
- All the statistical information of the adsorption energies for all the 158 metals/alloys and adsorbates in Statistics_AEDs_metals_bimetallics.csv and xlsx files. The data content is identical, files differs only by a format.
Files
AEDs_metals_bimetallics.csv
Files
(2.8 GB)
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Additional details
Additional titles
- Subtitle
- Dataset for: Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion
Related works
- Is supplement to
- Journal article: 10.1038/s41524-025-01664-9 (DOI)
Funding
- Research Council of Finland
- AI-guided CO2 Conversion / Consortium: AES 348179