Viscosity and phase boundary predictions through coarse grained molecular modelling

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The output of dynamic simulation programmes is a trajectory of configurations through time of all the molecules of interest and their respective arrangements against each other. In some cases, it is possible to extract physical quantities, such as diffusivity and viscosity, directly from the trajectories. In other cases, molecules may form into segregated aggregates, repeating microstructures, or any other types of patterns. In these cases, the key step in using these trajectories is to extract pattern information in some quantitative form and link these pattern descriptors to known physical properties. Such physical properties could include phase boundaries or stability observations. This constitutes a sort fo machine learning problem to pring out the full richness of dynamic simulation results.In this project, the success of the molecular modelling framework would be demonstrated by establishing relationships between the appearance of mesophases and the changes in viscosity, attempting to find ways of reproducing experimental observations. The fundamental understanding of these relationships within complex fluid processing is vital to several P&G businesses including Fabric and Home Care and Beauty.