重新加权Jarzynski采样：稀有事件的加速和自由能计算，从非平衡工作中学习到偏置势
Reweighted Jarzynski sampling: Acceleration of rare events and free energy calculation with a bias potential learned from nonequilibrium work
摘要：
我们介绍了一个简单的增强采样方法的自由能差和障碍沿一维反应坐标的计算。首先，沿着反应坐标进行少量的短非平衡模拟，并使用Jarzynski等式从非平衡功分布中学习近似自由能表面。这种自由能估计表示在一个紧凑的形式作为一个人工神经网络，并作为一个外部偏置电位，以加速罕见的事件在随后的分子动力学模拟。最后的自由能估计，然后通过重新加权的平衡概率分布的反应坐标下采样的外部偏压的影响。我们应用我们的重新加权Jarzynski采样配方的四个过程中的不同尺度和复杂的化学反应，在气相中，对协会的解决方案，和液滴成核过饱和蒸汽。在所有情况下，我们发现重新加权Jarzynski采样是一个非常有效的策略，导致快速收敛的自由能高精度。
英文摘要：
We introduce a simple enhanced sampling approach for the calculation of free energy differences and barriers along a one-dimensional reaction coordinate. First, a small number of short nonequilibrium simulations are carried out along the reaction coordinate, and the Jarzynski equality is used to learn an approximate free energy surface from the nonequilibrium work distribution. This free energy estimate is represented in a compact form as an artificial neural network and used as an external bias potential to accelerate rare events in a subsequent molecular dynamics simulation. The final free energy estimate is then obtained by reweighting the equilibrium probability distribution of the reaction coordinate sampled under the influence of the external bias. We apply our reweighted Jarzynski sampling recipe to four processes of varying scales and complexities-spanning chemical reaction in the gas phase, pair association in solution, and droplet nucleation in supersaturated vapor. In all cases, we find reweighted Jarzynski sampling to be a very efficient strategy, resulting in rapid convergence of the free energy to high precision.
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