We propose a workshop to bring together active researchers in the area of applications of dissipative particle dynamics (DPD) to soft matter systems, focusing particularly on aqueous systems, surfactants, and polymers.  The aim is to sustain a coherent community in this area, provide a venue to share best practices, identify key challenges, and build out a road map for future work.  A particularly desirable outcome would be to identify machine learning approaches that can be deployed in this context.

First proposed in the early 1990s, DPD has steadily progressed to the point where it can be used to make reliable predictions of the behavior of at least some kinds of soft matter systems.  This has been aided by a steady systematization in molecular fragmentation strategies and the development of coherent sets of transferable ‘force field’ parameters [1-3].  Our own work in this area has largely focused on predicting the phase behavior and critical micelle concentrations of aqueous surfactants, which we are now able to address with a degree of sophisticated chemical specificity [3-5].  An outstanding challenge though, beyond improving and extending the parameter sets, is undoubtedly to broaden out these methodologies to include polymers.  In particular, water soluble polymers in combination with surfactants form the backbone of a huge number of commercial applications ranging from home and personal care settings, through industrial processes, to biomedical applications. Additionally we note a far-reaching societal and industrial imperative for scientific progress is the urgent need to support decarbonisation of these markets, moving away from petrochemical and traditional plant-based feedstocks towards sustainably-sourced raw materials. Here the development of digital tools such as those using coarse-grained molecular dynamics methods like DPD is a key accelerator for innovation. Computer-aided formulation and design practices can be used to screen de novo green chemistries, improve the ability to deal with commercially-sourced novel raw materials, de-risk scale-up, and hone raw materials specifications.

We recently undertook a systematic review of the state of the art in particle-based simulations of surfactants, (Current Opinion in Colloid and Interface Science [6]), and this helps us motivate some of the key challenges which we hope to address in this workshop. 

Despite demonstrable and steady scientific progress, and early adoption by industry, there remain many open foundational questions particularly around achieving chemical specificity:

• what should be the balance between bottom-up and top-down parametrisation?
• how do we deal with special cases (for example intramolecular hydrogen bonds)?
• how best to incorporate Hofmeister series effects (ion specificity)?
• how best to use liquid state theory?
• what role could many-body DPD play?
• can we be predictive for kinetics (eg micelle lifetimes, dynamic interfacial tension)?
• how do we deal with surfaces?
• What benefits do machine learning and AI have to offer [7]?