Accelerating Formulated Product Design by Computer Aided Approaches
02 May 2017
- Rick Anderson



Computer Aided Formulation


​​​​​​Figure 1: Surfactant molecules adsorbed on hydrophobic silica



Complex formulated products, such as shampoos, detergent powders and liquids, processed foods, paints, adhesives, lubricants and pesticide granules, are ubiquitous in everyday and industrial life. The UK market for formulated products is worth around £180bn a year, with further potential in emerging overseas markets of around £1,000bn (Chemistry Innovation KTN Strategy Report 2010). Whilst in civil and mechanical engineering, the design process is done almost entirely by computer with reduced physical prototyping formulated product design is still predominantly an ad hoc labour-intensive process. Scientists from the Computational Chemistry Group in SCD have teamed up with Unilever, Syngenta and Infineum, to secure a TSB backed £1 million project to advance the development of formulated products using Computer Aided Formulation (CAF).

Active Research

The CAF project will build upon the potential of mesoscale modelling to drive a radical change in speed of formulated product design for manufacturability and in-use performance. The technical challenge is to develop predictive models that are simple yet accurate enough to enable predictive product design. This will reduce the time to market and development costs of a new or reformulated product. Initial results within the consortium across an extended formulation space (detergents, speciality chemicals, agro-technical formulations) are encouraging, to the point where the solution behaviour, formulation stability and surface interaction properties are accurately predicted for small subsets of chemical entities. The research team from the SCD will expand the capabilities of the established mesoscale method, dissipative particle dynamics (DPD), by incorporating appropriate physical models required to reproduce important experimental behaviour. Developments made to the DPD methodology during this project will address a number of deficiencies within the current framework and will broaden the range of applicability of this method. These developments will permit the simulation of more realistic and complex systems and will provide greater understanding into the mode of action of many different formulated products.

Derived Benefit

It is expected that there will be a number of positive outcomes from this project beyond the immediate commercial partners, who stand to develop better products faster and more sustainably. Academic beneficiaries from the field of condensed matter will be able to make the most of the advances to the DPD methodology and it is anticipated that the developments will be of use to researchers in other areas such as biophysics, e.g. the simulation of biomembranes and lipid bilayers. Furthermore, it is expected that DPD will play an ever-increasing role in multi-scale modelling approaches through bridging of the atomistic and continuum scales. In such approaches, atomistic simulations are performed to build the DPD models, followed by DPD simulations that provide the necessary input to the continuum codes. Implementations of such approaches can circumvent assumptions in the continuum codes since the mesoscale simulations can provide more accurate estimates of the thermodynamic state within the localised regions compared to a constitutive equation. With an improved DPD framework resulting from this work even more accurate estimates can be achieved. DPD methodology developments occurring during the project will be fully incorporated into the UK academic DPD simulation code, DL_MESO. This simulation code is freely available to academic users world-wide under license.

For more information please contact Rick Anderson.


M A Seaton, R L Anderson, S Metz and W Smith, DL_MESO: Highly scalable mesoscale simulation, Molecular Simulation 39, 2013, 796 – 821.

M A Johnston, W C Swope, K E Jordan, P B Warren, M G Noro, D J Bray, R L Anderson, Towards a Standard Protocol for Micelle Simulation, Journal of Physical Chemistry B 120, 2016, 6337 - 6351.

Contact: Anderson, Richard (STFC,DL,HC)

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