Enhanced sampling algorithms are indispensable when working with highly disconnected multimodal distributions. An important application of these is the conformational exploration of particular internal degrees of freedom of molecular systems. However, despite the existence of many commonly used enhanced sampling algorithms to explore these internal motions, they often rely on system-dependent parameters, which negatively impact efficiency and reproducibility. Here, we present fully adaptive simulated tempering (FAST), a variation of the irreversible simulated tempering algorithm, which continuously optimizes the number, parameters, and weights of intermediate distributions to achieve maximally fast traversal over a space defined by the change in a predefined thermodynamic control variable such as temperature or an alchemical smoothing parameter. This work builds on a number of previously published methods, such as sequential Monte Carlo, and introduces a novel parameter optimization procedure that can, in principle, be used in any expanded ensemble algorithms. This method is validated by being applied on a number of different molecular systems with high torsional kinetic barriers. We also consider two different soft-core potentials during the interpolation procedure and compare their performance. We conclude that FAST is a highly efficient algorithm, which improves simulation reproducibility and can be successfully used in a variety of settings with the same initial hyperparameters.

中文翻译：

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使用完全自适应模拟回火增强扭转采样

在处理高度断开的多峰分布时，增强采样算法是必不可少的。这些的一个重要应用是分子系统特定内部自由度的构象探索。然而，尽管存在许多常用的增强采样算法来探索这些内部运动，但它们通常依赖于系统相关的参数，这会对效率和再现性产生负面影响。在这里，我们提出了完全自适应模拟回火（FAST），这是不可逆模拟回火算法的一种变体，它不断优化中间分布的数量、参数和权重，以实现在由预定义热力学变化定义的空间上的最大快速遍历控制变量，例如温度或炼金术平滑参数。这项工作建立在许多先前发布的方法（例如顺序蒙特卡罗）的基础上，并引入了一种新颖的参数优化过程，原则上可以在任何扩展的集成算法中使用。该方法通过应用于许多具有高扭转动力学势垒的不同分子系统而得到验证。我们还在插值过程中考虑了两种不同的软核潜力并比较了它们的性能。我们得出的结论是，FAST 是一种高效的算法，它提高了模拟的再现性，并且可以成功地用于具有相同初始超参数的各种设置中。