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
- Container
- Faraday Discussions
- Publisher
- Royal Society of Chemistry (RSC)
- 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 = {Matthew S. Johnson and Richard H. West and Judit Zádor},
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 - Matthew S. Johnson AU - Richard H. West AU - Judit Zádor PY - 2026 JO - Faraday Discussions DO - 10.1039/d5fd00143a UR - https://doi.org/10.1039/d5fd00143a ER -
APA
Johnson, M. S., West, R. H., & Zádor, J. (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
- crossref · retrieved 2026-09-25T00:29:42.800Z