Unified physio-thermodynamic descriptors via learned CO2 adsorption properties in metal-organic frameworks
Abstract The large design space of metal-organic frameworks (MOFs) has prompted the utilization of deep learning to drive material design. Nonetheless, the prediction of key thermodynamic properties, such as heat of adsorption ( $$\Delta {H}_{{\rm{ads}}}$$ Δ H ads ), remains largely unexplored for C...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Portfolio
2025-07-01
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| coleção: | npj Computational Materials |
| Acesso em linha: | https://doi.org/10.1038/s41524-025-01700-8 |
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