The relationship between saturate, aromatic, resin, and asphaltene (SARA) contents and asphalt properties remains unclear. This study aimed to propose a high-throughput molecular dynamics simulation framework and demonstrate its application in rapidly building asphalt molecular models of various SARA ratios and predicting their properties, using density as an example. Based on the framework, 400 models with varying SARA ratios with different aging degrees were generated to calculate their densities and used to train machine learning algorithms. The ordinary least squares model achieved R² values exceeding 80%, and quantitative formulas linking asphalt density to SARA ratios were derived. It was found that saturate content negatively correlates with asphalt density, while resin content positively correlates with asphalt density. Additionally, asphalt density and viscosity increase with aging, influenced simultaneously by the SARA ratio and aging degree. Overall, this paper creates a rapid, high-throughput molecular simulation pathway to predict asphalt behavior.

中文翻译：

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通过高通量分子动力学模拟预测沥青性能


饱和烃、芳香烃、树脂和沥青质 (SARA) 含量与沥青性能之间的关系尚不清楚。本研究旨在提出一种高通量分子动力学模拟框架，并以密度为例，展示其在快速构建各种SARA比例的沥青分子模型并预测其性能方面的应用。基于该框架，生成了400个具有不同SARA比率和不同老化程度的模型来计算它们的密度并用于训练机器学习算法。普通最小二乘模型实现了超过80%的R ²值，并导出了将沥青密度与SARA比率联系起来的定量公式。研究发现，饱和物含量与沥青密度呈负相关，而树脂含量与沥青密度呈正相关。此外，沥青密度和粘度随着老化而增加，同时受到SARA比率和老化程度的影响。总的来说，本文创建了一种快速、高通量的分子模拟途径来预测沥青行为。