An interpretable machine learning framework for prediction of adsorption energies and generative design of active sites on arbitrary catalysts.
- DOI
- 10.1039/d5fd00143a
- Published
- 2026 Sep 17
- Container
- Faraday discussions
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1039/d5fd00143a,
title = {An interpretable machine learning framework for prediction of adsorption energies and generative design of active sites on arbitrary catalysts.},
author = {Johnson MS and West RH and Zádor J},
year = {2026},
journal = {Faraday discussions},
doi = {10.1039/d5fd00143a},
url = {https://doi.org/10.1039/d5fd00143a}
}RIS
TY - JOUR TI - An interpretable machine learning framework for prediction of adsorption energies and generative design of active sites on arbitrary catalysts. AU - Johnson MS AU - West RH AU - Zádor J PY - 2026 JO - Faraday discussions DO - 10.1039/d5fd00143a UR - https://doi.org/10.1039/d5fd00143a ER -
APA
MS, J., RH, W., & J, Z. (2026). An interpretable machine learning framework for prediction of adsorption energies and generative design of active sites on arbitrary catalysts.. Faraday discussions. https://doi.org/10.1039/d5fd00143a
Source records
- pubmed · retrieved 2026-11-10T07:12:52.048Z