Accelerating Consumer Goods Formulation with Advanced Mesoscale Simulations

The challenge

The efficacy of consumer products, from hair conditioners to laundry detergents, is determined by their underlying molecular architecture. Central to this is the self-assembly of surfactant molecules into dynamic structures, principally micelles, which governs key product attributes like stability and performance. For many applications, performance is dictated by how these micelles interact with and deposit onto surfaces, such as hair fibres or fabrics. Gaining predictive control over this entire process, from assembly in solution to surface deposition, represents a significant scientific challenge and a key driver of industrial innovation. Mesoscale modelling provides the ideal framework to unravel this complexity.

To spearhead advances in this field, we invite applications for a PhD position in mesoscale computational modelling. This project will bridge the gap from fundamental molecular interactions to macroscopic product performance, establishing a robust modelling capability to accelerate the development of next-generation formulations in consumer goods industry.

The Project

While essential, experimental characterization of new formulations is often resource intensive. Computational modelling offers a powerful, complementary approach to accelerate the design-test-learn cycle. This PhD project aims to pioneer a systematic and automated computational framework that overcomes key limitations in current simulation methods. The research will focus on advancing Many-Body Dissipative Particle Dynamics (MDPD), a state-of-the-art coarse-graining technique for mesoscale simulations.

At the heart of this project is the challenge of parameterization: teaching the model to faithfully capture underlying chemical properties. Your research will establish systematic approaches for representing the complex interactions within formulated products, developing robust models for charged species and water to capture essential solvation and electrostatic effects. The scope includes extending the MDPD formalism to account for components of varying sizes and shapes (a critical feature for modelling realistic multicomponent mixtures) by advancing the underlying theory to ensure it accurately reflects the system's thermodynamic behavior, correctly capturing both enthalpic and entropic driving forces for self-assembly.

A primary deliverable will be an efficient and automated parameterization workflow, dramatically reducing the reliance on time-consuming calculations and expert intervention. You will implement and validate faster methods, such as group contribution schemes, for determining model parameters from molecular-level data. The framework will also be generalized to incorporate corrections for bonded interactions within molecules. Ultimately, the predictive power of the developed models will be rigorously benchmarked against established experimental data for key properties, such as critical micelle concentration and interfacial tension, in industrially relevant surfactant systems, proving the real-world utility of your work.