AI 中文总结
本文提出名为EquiNET的方法，通过重复测量轨迹涨落确定熵产生或其下界，进而估计自由能差，在高耗散区域等传统方法失效的场景仍能给出准确结果。
AI 中文摘要
平衡自由能差可通过涨落定理（如Jarzynski等式和Crooks涨落定理）从非平衡功涨落中测量，但这些方法在高耗散区域统计效率低下，因为其收敛需要负熵产生的轨迹，而这类事件呈指数级罕见。本文证明，重复测量轨迹涨落足以在不依赖此类罕见事件的情况下确定熵产生，或在仅能进行部分测量时确定其下界；该信息可给出自由能差的精确估计或严格界。我们通过数值模拟（包括生物分子折叠与解折叠的模拟）验证了名为EquiNET的该方法，结果显示其在传统方法失效的区域仍能恢复准确的自由能估计值。
英文摘要
Equilibrium free-energy differences can be measured from nonequilibrium work fluctuations using fluctuation theorems, such as the Jarzynski equality and the Crooks fluctuation theorem. However, these approaches become statistically inefficient in high-dissipation regimes because their convergence requires trajectories with negative entropy production, events that occur exponentially rarely. Here, we demonstrate that repeated measurements of trajectory fluctuations are sufficient to determine entropy production without relying on such rare events, or to determine a lower bound on it when only partial measurements are available. This information then yields exact estimates of free-energy differences, or rigorous bounds. We validate this approach, named EquiNET, with numerical simulations, including one of biomolecular folding and unfolding, showing that it recovers accurate free-energy estimates even in regimes where conventional approaches fail.
Comments15 pages, 4 figures
