Industrial chemical formulations are often slow and expensive iterative methods, requiring many scientists many hours of work. In silico methods have the opportunity to aid the laboratory decision-making processes, and hence to accelerate the formulation process. We are generating technologies that enable easy access to such in silico methods, the output being automated experiments that provide complementary information to laboratory scientists.
A micelle is a self-assembled macro-molecular structure formed from amphipatic single molecules. This means that, at certain concentrations, molecules formed of a hydrophobic (water adverse) tail and a hydrophilic (water auspicious) head groups can form molecular structures spontaneously when mixed with an appropriate solvent. This is important in life science and industrial chemistry, as molecules such as lipids, which form bilayers making up cell membranes, and surfactants, which are the key constituents of soap, are two prominent examples of micelle-forming molecules.
We are currently applying a range of simulation methods to gain chemical insight, such as formation mechanisms and structure, of these assemblies. We apply all atom simulations to elucidate the atomic detail and coarse-grained methods to study formation mechanisms.
Automated virtual experiments are being constructed in silico to measure the viscosity of formulations. Such coarse-grained molecular simulations can quantify the delicate interplay between the interactions of the ingredients, formulation structures and rheological properties under arbitrary mechanical stressing conditions and mixture concentrations.
These computational models serve to instruct the industrial chemist which formulation combinations can be applied to produce products with the desired bespoke deformation properties. This is only made possible by leveraging the latest IBM power-based high-performance computing (HPC) systems and cognitive optimisation methodologies.