Viscosities of experimental aqueous glycerol mixtures

In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. PyThermoML enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR. The usage of pyThermoML is demonstrated in the following example workflow and can be utilized to read the given ThermoML file.

为使复杂液体混合物的热物理性质可供全面分析，本研究团队基于标准数据交换格式ThermoML，开发了一套数据管理和分析平台。通过系统地研究水含量在整个组成范围内以及温度介于278.15至318.15K之间对密度和扩散系数的影响，验证了将实验与模拟所得的热物理数据集成的可行性，并以甲醇-水、甘油-水两种二元混合物为例进行了展示。实验数据系手动从文献中提取。在同一参数空间内，通过全面的分子动力学模拟进行了探索，其结果直接导入分析平台。数据集成的优势通过评估为纯化合物开发的力场在不同组成和温度下的可迁移性，以及通过分析甲醇-水、甘油-水混合物的过剩混合性质作为非理想性的度量来体现。数据管理和分析平台的核心是全新开发的Python库pyThermoML，该库将元数据、参数以及实验测定或模拟的热物理性质表示为Python数据类。平台展示了从数据采集到全面数据分析的流畅数据流可行性。PyThermoML使得数据集具有互操作性和可重用性。在斯图加特大学（University of Stuttgart）的Dataverse安装上发布ThermoML文档，使得热物理数据可被检索和访问，从而实现FAIR原则。以下示例工作流程展示了pyThermoML的使用，并可应用于读取给定的ThermoML文件。