Molecular diffusion coefficients calculated using molecular dynamics (MD) simulations suffer from finite-size (i.e., finite box size and finite particle number) effects. Results from finite-sized MD simulations can be upscaled to infinite simulation size by applying a correction factor. For self-diffusion of single-component fluids, this correction has been well-studied by many researchers including Yeh and Hummer (YH); for binary fluid mixtures, a modified YH correction was recently proposed for correcting MD-predicted Maxwell–Stephan (MS) diffusion rates. Here we use both empirical and machine learning methods to identify improvements to the finite-size correction factors for both self-diffusion and MS diffusion of binary Lennard-Jones (LJ) fluid mixtures. Using artificial neural networks (ANNs), the error in the corrected LJ fluid diffusion is reduced by an order of magnitude versus existing YH corrections, and the ANN models perform well for mixtures with large dissimilarities in size and interaction energies where the YH correction proves insufficient.

中文翻译：

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基于机器学习的二元流体有限尺寸分子动力学扩散模拟的放大

使用分子动力学（MD）模拟计算出的分子扩散系数受到有限尺寸（即有限盒子尺寸和有限粒子数）的影响。通过应用校正因子，可以将有限尺寸的MD仿真的结果放大到无限的仿真尺寸。对于单组分流体的自我扩散，许多研究人员都对这种校正进行了深入研究，其中包括Yeh和Hummer（YH）。对于二元流体混合物，最近提出了一种改进的YH校正方法，用于校正MD预测的Maxwell–Stephan（MS）扩散速率。在这里，我们使用经验和机器学习方法来确定对二元Lennard-Jones（LJ）流体混合物的自扩散和MS扩散的有限尺寸校正因子的改进。使用人工神经网络（ANN），