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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...

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Detalles Bibliográficos
Autores principales: Wang, Yihang, Herron, Lukas, Tiwary, Pratyush
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
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

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