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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| Auteurs principaux: | , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/a84d6d528971441da8fc0e3f5f04e7fe |
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| Résumé: | 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 where two-body interactions among elementary components are nonnegligible. |
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