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

  • 1. ROR icon Aalto University
  • 2. EDMO icon Technical University of Munich
  • 3. ROR icon University of Turku

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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

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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