Using a comprehensive set of recently published experimental results for training and validation, we have developed computational models appropriate for simulations of aqueous solutions of poly(ethylene oxide) alkyl ethers, an important class of micelle- forming nonionic surfactants, usually denoted CnEm. These models are suitable for use in simulations that employ a moderate amount of coarse graining and especially for dissipative particle dynamics (DPD), which we adopt in this work.

The experimental data used for training and validation were reported earlier and produced in our laboratory using dynamic light scattering (DLS) measurements per- formed on twelve members of the CnEm compound family yielding micelle size dis- tribution functions and mass weighted mean aggregation numbers at each of several surfactant concentrations. The range of compounds and quality of the experimental results were designed to support the development of computational models. An es- sential feature of this work is that all simulation results were analysed in a way that is consistent with the experimental data. Proper account is taken of the fact that a broad distribution of micelle sizes exists, so mass weighted averages (rather than num- ber weighted averages) over this distribution are required for the proper comparison of simulation and experimental results.

The resulting DPD force field reproduces several important trends seen in the exper- imental critical micelle concentrations and mass averaged mean aggregation numbers with respect to surfactant characteristics and concentration. We feel it can be used to investigate a number of open questions regarding micelle sizes and shapes and their dependence on surfactant concentration for this important class of nonionic surfactants.

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3 results in optimisation in Keywords: optimisation

Bayesian Illumination: Inference and Quality-Diversity Accelerate Generative Molecular Models

A Model for the Simulation of the CnEm Nonionic Surfactant Family Derived from Recent Experimental Results

Race to the bottom: Bayesian optimisation for chemical problems

3 results in Keywords: optimisation