Learning the best nanoscale heat engines through evolving network topology

While the thermodynamic power and efficiency of nanoscale heat engines in noninteracting regimes has been well-explored, revealing effect of many-body interactions remains a challenge. Here, the authors develop a reinforcement learning framework to achieve optimal power and efficiency in nanoengines...

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Autores principales: Yuto Ashida, Takahiro Sagawa
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/a84d6d528971441da8fc0e3f5f04e7fe
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