加速化学势计算的最小归一化流
Accelerating Chemical Potential Calculations with Minimal Normalizing Flows
AI总结：
提出最小归一化流（MNF），通过低维物理变换加速化学势计算，在Lennard-Jones体系中实现至少10倍加速，在水合离子体系中有效样本量提升3-8倍。
摘要：
化学势是分子模拟中最重要的性质之一，因为它们定义了许多技术相关的集体性质。溶液中物种的化学势通过计算将该物种添加到体相系统中的自由能变化获得，对于电解质等系统，由于“未插入”和“插入”状态之间缺乏相空间重叠，这种计算通常非常昂贵。最近，归一化流被引入作为一种加速自由能计算的方法，通过学习一个双射函数（尽可能具有表达性），将一个玻尔兹曼分布的构型空间映射到另一个。这种表达性使得它们难以训练，限制了它们为任何新系统“即时”生成的能力，在实践中，这些映射对液体的采样改进仅表现出适度的效果。我们通过引入“最小”归一化流（MNF）来解决这些问题。这是一种可训练的双射映射，有意限制其表达性，而是应用低维、物理信息变换。由于其简单性以及我们引入的新训练策略，有用的MNF可以在1分钟的GPU时间内训练完成。我们展示了如何通过简单的径向映射将Lennard-Jones粒子系统的化学势计算加速至少10倍。我们还对水中离子溶剂化应用了径向和定向映射，表明MNF可以将充电自由能计算的有效样本量提高3倍，将由于力场扰动引起的自由能变化计算的有效样本量提高8倍。这为开发物理信息映射奠定了基础，这些映射可以在保持低训练成本的同时加速复杂的自由能计算。
英文摘要：
Chemical potentials are among the most important properties that can be obtained from a molecular simulation since they define many technologically relevant collective properties. The chemical potential of a species in solution is obtained by computing the free energy change of adding that species into a bulk system, a calculation typically very expensive for systems such as electrolytes, due to the lack of phase space overlap between "not-inserted" and "inserted" states. Recently, normalizing flows have been introduced as a way to accelerate free energy computations by learning a bijective function, constructed to be as expressive as possible, that maps the configuration space of one Boltzmann distribution onto another. This expressivity makes them difficult to train, limiting their ability to be generated "on-the-fly" for any new system, and in practice these mappings have shown only modest sampling improvements for liquids. We address these issues by introducing a "minimal" normalizing flow (MNF). This is a trainable bijective mapping that is intentionally limited in expressivity, and instead applies low-dimensional, physically informed transformations. Useful MNFs can be trained in 1 minute of GPU time due to their simplicity and our introduction of a novel training strategy. We show how calculations of chemical potentials of Lennard-Jones particle systems can be accelerated by at least 10 times with a simple radial mapping. We also apply a radial and orientational mapping to ion solvation in water, showing that MNFs can increase the effective sample size by 3 times for charging free energy calculations and 8 times for calculating free energy changes due to force field perturbations. This provides the foundation for the development of physically-informed mappings that can accelerate complex free energy calculations while retaining low training costs.
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