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From data to noise to data for mixing physics across temperatures with generative artificial intelligence
Using simulations or experiments performed at some set of temperatures to learn about the physics or chemistry at some other arbitrary temperature is a problem of immense practical and theoretical relevance. Here we develop a framework based on statistical mechanics and generative artificial intelli...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
National Academy of Sciences
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371742/ https://www.ncbi.nlm.nih.gov/pubmed/35925885 http://dx.doi.org/10.1073/pnas.2203656119 |