Department of Chemistry, Stanford University, Stanford, California 94305, USA.
Phys Rev Lett. 2023 Mar 10;130(10):107101. doi: 10.1103/PhysRevLett.130.107101.
Controlling thermodynamic cycles to minimize the dissipated heat is a long-standing goal in thermodynamics, and more recently, a central challenge in stochastic thermodynamics for nanoscale systems. Here, we introduce a theoretical and computational framework for optimizing nonequilibrium control protocols that can transform a system between two distributions in a minimally dissipative fashion. These protocols optimally transport a system along paths through the space of probability distributions that minimize the dissipative cost of a transformation. Furthermore, we show that the thermodynamic metric-determined via a linear response approach-can be directly derived from the same objective function that is optimized in the optimal transport problem, thus providing a unified perspective on thermodynamic geometries. We investigate this unified geometric framework in two model systems and observe that our procedure for optimizing control protocols is robust beyond linear response.
控制热力学循环以最小化耗散热量是热力学中长期以来的目标，最近也是纳米尺度系统随机热力学中的一个核心挑战。在这里，我们引入了一个理论和计算框架，用于优化非平衡控制协议，可以以最小耗散的方式将系统在两个分布之间转换。这些协议以通过概率分布空间的路径最优地传输系统，这些路径最小化转换的耗散成本。此外，我们表明，可以通过线性响应方法确定的热力学度量-直接从最优传输问题中优化的目标函数中得出，从而为热力学几何形状提供了统一的视角。我们在两个模型系统中研究了这个统一的几何框架，并观察到我们优化控制协议的过程在超出线性响应的情况下是稳健的。
