We present an approach that extends the theory of targeted free energy perturbation (TFEP) to calculate free energy differences and free energy surfaces at an accurate quantum mechanical level of theory from a cheaper reference potential. The convergence is accelerated by a mapping function that increases the overlap between the target and the reference distributions. Building on recent work, we show that this map can be learned with a normalizing flow neural network, without requiring simulations with the expensive target potential but only a small number of single-point calculations, and, crucially, avoiding the systematic error that was found previously. We validate the method by numerically evaluating the free energy difference in a system with a double-well potential and by describing the free energy landscape of a simple chemical reaction in the gas phase.

中文翻译：

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重新审视目标自由能扰动：来自映射参考电位的准确自由能

我们提出了一种扩展目标自由能扰动 (TFEP) 理论的方法，以从更便宜的参考电位在精确的量子力学理论水平上计算自由能差异和自由能面。收敛由一个映射函数加速，该函数增加了目标和参考分布之间的重叠。在最近的工作的基础上，我们表明可以使用归一化流神经网络学习该地图，而无需使用昂贵的目标潜力进行模拟，而只需进行少量的单点计算，并且至关重要的是，避免了发现的系统误差之前。