Nanoporous materials serve as very effective media for storing and separating small molecules. To design the best materials for a given application based on adsorption, one usually assesses the equilibrium performance by using key thermodynamic quantities such as Henry constants or adsorption loading values. To go beyond standard methodologies, we probe here the transport effects occurring in the material by studying the self-diffusion coefficients of xenon inside the nanopores of the framework materials. We find good correlations between the diffusion coefficients and the pore aperture size as well as other geometrical and energetic descriptors. We used extensive molecular dynamics simulations to calculate the diffusion coefficient of xenon in 4873 MOFs from the CoRE MOF 2019 database, the first large-scale database of transport properties published at this scale. Based on these data, we present a tool to quickly evaluate the diffusion energy barrier that proved to be very correlated to the diffusion rate. This descriptor, alongside other geometrical characterizations, was then used to build a machine learning model that can predict the xenon diffusion coefficients in MOFs. The final trained model is quite accurate and shows a root-mean-square error on the log₁₀ of the diffusion coefficient equal to 0.25.

中文翻译：

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基于过渡态理论描述子的机器学习预测扩散系数

纳米多孔材料是储存和分离小分子的非常有效的介质。为了根据吸附设计针对给定应用的最佳材料，通常通过使用关键热力学量（例如亨利常数或吸附负载值）来评估平衡性能。为了超越标准方法，我们通过研究框架材料纳米孔内氙的自扩散系数来探讨材料中发生的传输效应。我们发现扩散系数和孔径大小以及其他几何和能量描述符之间存在良好的相关性。我们使用广泛的分子动力学模拟来计算 CoRE MOF 2019 数据库中 4873 个 MOF 中氙的扩散系数，这是第一个以这种规模发布的大型传输特性数据库。基于这些数据，我们提出了一种快速评估扩散能垒的工具，事实证明该工具与扩散速率密切相关。然后，将该描述符与其他几何特征一起用于构建机器学习模型，该模型可以预测 MOF 中的氙扩散系数。最终训练的模型非常准确，扩散系数的 log ₁₀的均方根误差等于 0.25。