Microstructural properties of wormlike micelles (WLMs), which are employed in characterizing the system to predict rheological properties, have long been obtained via elusive experiments such as small-angle neutron scattering. Hence, in this work, a framework to explicitly obtain such properties from macroscopic rheology measurements was developed. Specifically, the parameters of a mesoscopic pointer-based algorithm, which can predict the linear rheology of WLMs were obtained with the aid of a multi-scale multi-recommendation (MSMR) batch Bayesian optimization (BO) methodology. From three case studies, it was observed that the MSMR batch BO was able to obtain a set of parameters, which showed high prediction accuracy, in comparison to a sequential BO. Specifically, it was found that microstructural properties such as persistent length and the diameter of WLMs were successfully predicted by the proposed framework. Hence, this framework can be utilized in characterizing various WLM systems from readily available macroscopic rheological measurements.

中文翻译：

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通过多尺度多推荐批量贝叶斯优化估计蠕虫状胶束的微观结构特性

蠕虫状胶束 (WLM) 的微观结构特性用于表征系统以预测流变特性，长期以来一直通过难以捉摸的实验（例如小角度中子散射）获得。因此，在这项工作中，开发了从宏观流变学测量中明确获得此类特性的框架。具体而言，借助多尺度多推荐 (MSMR) 批量贝叶斯优化 (BO) 方法，获得了基于细观指针的算法的参数，该算法可以预测 WLM 的线性流变。从三个案例研究中，观察到 MSMR 批量 BO 能够获得一组参数，与顺序 BO 相比，这些参数显示出较高的预测准确性。具体来说，发现所提出的框架成功地预测了微结构特性，例如持续长度和 WLM 的直径。因此，该框架可用于从现成的宏观流变测量中表征各种 WLM 系统。