Tracer-diffusion coefficients of experimental aqueous methanol 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.

为使复杂液态混合物的热物理性质能够全面分析，本团队基于标准数据交换格式ThermoML，开发了一套数据管理与分析平台。通过系统研究水含量在全组分范围内以及278.15至318.15K温度范围内对密度和扩散系数的影响，验证了将实验与模拟数据整合的实用性，以甲醇-水及甘油-水两种二元混合物为例。实验数据系从文献中手工提取。同时，通过全面分子动力学模拟探索了相同参数空间，并将模拟结果直接导入分析平台。数据整合的优势通过评估针对纯净化合物开发的力场在不同组分和温度下的可迁移性，以及通过分析甲醇-水及甘油-水混合物的过剩混合性质来衡量非理想性的程度得到了展示。数据管理与分析平台的核心是新开发的Python库pyThermoML，该库以Python数据类形式表示元数据、参数以及实验测定或模拟得到的热物理性质。该平台实现了从数据采集到全面数据分析的无缝数据流可行性。PyThermoML促进了数据集的互操作性和复用性。在斯图加特大学（University of Stuttgart）的Dataverse安装上发布ThermoML文档，使得热物理数据易于发现和获取，从而实现FAIR原则。以下示例工作流程展示了pyThermoML的使用方法，并可应用于读取提供的ThermoML文件。