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.