Hydrogen, Oxygen, and Lead Adsorbates on Al<sub>13</sub>Co<sub>4</sub>(100): Accurate Potential Energy Surfaces at Low Computational Cost by Machine Learning and DFT-Based Data
- DOI
- 10.1021/acs.jctc.4c00367
- Published
- 2024-08-19
- Container
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jctc.4c00367,
title = {Hydrogen, Oxygen, and Lead Adsorbates on Al<sub>13</sub>Co<sub>4</sub>(100): Accurate Potential Energy Surfaces at Low Computational Cost by Machine Learning and DFT-Based Data},
author = {Nathan Boulangeot and Florian Brix and Frédéric Sur and Émilie Gaudry},
year = {2024},
journal = {Journal of Chemical Theory and Computation},
doi = {10.1021/acs.jctc.4c00367},
url = {https://doi.org/10.1021/acs.jctc.4c00367}
}RIS
TY - JOUR TI - Hydrogen, Oxygen, and Lead Adsorbates on Al<sub>13</sub>Co<sub>4</sub>(100): Accurate Potential Energy Surfaces at Low Computational Cost by Machine Learning and DFT-Based Data AU - Nathan Boulangeot AU - Florian Brix AU - Frédéric Sur AU - Émilie Gaudry PY - 2024 JO - Journal of Chemical Theory and Computation DO - 10.1021/acs.jctc.4c00367 UR - https://doi.org/10.1021/acs.jctc.4c00367 ER -
APA
Boulangeot, N., Brix, F., Sur, F., & Gaudry, É. (2024). Hydrogen, Oxygen, and Lead Adsorbates on Al<sub>13</sub>Co<sub>4</sub>(100): Accurate Potential Energy Surfaces at Low Computational Cost by Machine Learning and DFT-Based Data. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.4c00367
Source records
- crossref · retrieved 2026-09-26T17:06:35.955Z