由结构因子计算化学势：I. 中性多组分混合物
Chemical potentials from structure factors: I. Neutral multi-component mixtures
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中文总结 AI 辅助
本文将S0方法推广至中性多组分混合物，通过扩展统计力学形式并结合高斯过程积分与主动学习，计算了熔融金属合金混合自由能及对乙酰氨基酚多晶型体溶解度，为化学势计算提供实用可扩展途径。
中文摘要 AI 辅助
多组分混合物的化学势是诸多物理和化学现象的基础，但其计算仍具挑战性。S0方法可通过平衡分子动力学模拟计算化学势，该方法利用粒子数涨落与化学势导数之间的热力学关系，再沿不同组分进行数值积分。本文将S0方法从双组分混合物推广至中性多组分混合物：首先将统计力学形式扩展至高维组分空间，随后引入高斯过程积分方案并结合主动学习，以高效积分化学势并采样多样组分。我们采用该方法计算了熔融金属合金的混合自由能，以及对乙酰氨基酚两种多晶型体在水-乙醇溶剂中的溶解度。扩展后的S0方法为从原子模拟计算中性体相多组分混合物的化学势提供了实用且可扩展的途径。
英文摘要
The chemical potentials of multi-component mixtures underlie many physical and chemical phenomena, but remain challenging to compute. The S0 method enables the computation of chemical potentials from equilibrium molecular dynamics simulations, by leveraging the thermodynamic relationship between particle number fluctuations and derivatives of chemical potentials, followed by numerical integration along different compositions. Here we generalize the S0 method from two-component mixtures to neutral multi-component mixtures. We first extend the statistical mechanical formalism to high-dimensional compositional space, and then introduce a Gaussian process integration scheme combined with active learning to efficiently integrate chemical potentials and sample diverse compositions. We use this method to compute the mixing free energies of a molten metal alloy, and the solubilities of two paracetamol polymorphs in water-ethanol solvents. The extended S0 method provides a practical and scalable route for computing chemical potentials in neutral bulk multi-component mixtures from atomistic simulations.
