Learning the best nanoscale heat engines through evolving network topology

Abstract

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.