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Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
The prediction of temperature effects from first principles is computationally demanding and typically too approximate for the engineering of high-temperature processes. Here, we introduce a hybrid approach combining zero-Kelvin first-principles calculations with a Gaussian process regression model...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636515/ https://www.ncbi.nlm.nih.gov/pubmed/34853301 http://dx.doi.org/10.1038/s41467-021-27154-2 |