AQCat25: unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis

Abstract Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data. To exten...

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Detalhes bibliográficos

Principais autores:	Omar Allam, Brook Wander, SungYeon Kim, Rudi Plesch, Tyler Sours, Jia-Min Chu, Thomas Ludwig, Jiyoon Kim, Rodrigo Wang, Shivang Agarwal, Alan Rask, Alexandre Fleury, Chuhong Wang, Andrew Wildman, Thomas Mustard, Kevin Ryczko, Paul Abruzzo, AJ Nish, Aayush R. Singh
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Nature Portfolio 2026-04-01
coleção:	npj Computational Materials
Acesso em linha:	https://doi.org/10.1038/s41524-026-02099-6
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