Overview
This project pioneers formulation design by integrating statistical optimisation tools with advanced molecular-scale simulations and industrial wet-chemistry validation. It will identify optimal detergent compositions from vast molecular design spaces, reveal how molecular chemistry drives performance, and accelerate the discovery of smarter, greener ingredients.
About this opportunity
Predicting optimal compositions in home-care products is a major challenge, given the vast design space of often high-value ingredients. In detergents, this requires understanding the complex aqueous chemistry of small molecular components (e.g., fragrance molecules) and how they interact with fibrous materials. Exploring this empirically for the thousands of components available to formulation design would be resource-intensive and impractical. This motivates the PhD project, which will exploit a combined data science–molecular simulation approach, cross-validated with wet chemistry, to optimise existing formulation products and, when combined with state-of-the-art cheminformatics approaches, design new ingredients for sustainable product innovation.
You will employ Bayesian optimisation to systematically reduce large catalogues of potential formulation ingredients based on key descriptors derived from existing data. For the selected subset, enhanced-sampling molecular dynamics approaches will be used to determine mechanisms and rates for the reversible binding of molecules to fibrous surfaces in wet and dry conditions—the key indicators of ingredient performance. The simulations will reveal how molecular topology and chemistry control penetration of the surfactant-rich interfacial layer at fibres during washing, and subsequent molecule release in air.
By integrating these molecular insights with Bayesian inference and cheminformatics, the computational tools developed in this project will enable the efficient selection and prediction of new formulation ingredients for direct evaluation in wet chemistry experiments carried out by industry partners. As such, we are seeking a highly motivated candidate with interests in molecular modelling, digital design for real-world problems, and combining advanced tools in data science with complex molecular-scale problems.
The project will be supervised by Dr Aaron Finney and Prof Simon Maskell from the School of Engineering at the University of Liverpool, and Dr Martin Crossman and Mr Tinto Alencherry from Unilever. The supervisory team brings complementary expertise spanning molecular simulation, Bayesian methods, interfacial science and cheminformatics, providing the full range of skills required to guide the research. Supported by Unilever, this PhD offers close collaboration with industrial R&D teams, linking predictive computational models to sustainable and efficient product innovation.
This project is expected to start in October 2026 and is offered under the EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry based in the Materials Innovation Factory at the University of Liverpool, the largest industry-academia colocation in UK physical science. The successful candidate will benefit from training in robotic, digital, chemical and physical thinking, which they will apply in their domain-specific research in materials design, discovery and processing. PhD training has been developed with 35 industrial partners and is designed to generate flexible, employable, enterprising researchers who can communicate across domains.