Hybrid AI-Simulation Mesoscopic Material Design

Background
The project investigates colloidal suspensions of complex particles, which have the ability to cluster together and form long-range ordered structures. Such colloidal nanomaterials are ubiquitous in industrial and technological applications ranging from foods, pesticides, paints, oil recovery, lubricants, medicines, pastes, detergents, cosmetics and creams. They are especially popular as thickening agents used for tuning the viscoelastic properties of various formulations.

Objectives
The PhD student will focus on the development and application of techniques that can automatically set up, simulate and accurately measure industrial mesoscopic colloidal structure property relationships, as well as coupling them to AI regression optimisation algorithms. The project adapts a dual hybrid AI-Simulation approach, whereby the colloidal simulations provide direct insight into the intricate relationship between the macroscopic of mesoscopic materials, and their microscopic formulation ingredients. At the same time, AI machine learning regression algorithms will automatically define and execute a series of these simulations to efficiently calculate the optimal formulation recipe for producing a colloidal mesoscopic material with the prescribed macroscopic properties.

Main Research questions
1. What is the phase behaviour of colloidal trimers interacting via the Mie potential?
2. What are the crystal phases that are observed at equilibrium?
3. What is their rheological response when an external field is applied?
4. What are the parameters that most influence the formation of these structures and their rheology?
5. How can the kinetics of self-assembly be optimized by AI regression optimisation algorithms?
6. How can we use these algorithms to minimise structural defects in nanomaterials?

Approach
The student will employ molecular simulation and modelling, as well AI regression optimisation algorithms to study equilibrium and out-of-equilibrium properties of colloidal suspensions. Spe cifically:

1. Molecular Dynamics simulations to calculate the phase diagram of Mie-like colloidal trimers (on-going - liquid/vapour region already identified)
2. Non-equilibrium MD simulations to study the effect of an external field (e.g. shear) on the stability of the structures
3. AI regression optimisation algorithms


Novel content
The project aims at bridging colloidal science with computer science by providing a computational apparatus able to identify the most suitable kinetic paths for the synthesis of nanomaterials. Once a material property of interest is identified, our vision is to find the most suitable set of particles, inter-particle interactions and physico-chemical properties, that would provide a nanomaterial with specific characteristics, performance and response. To this end, we need algorithms able to manage substantial amount of data and to identify the most suitable parameters leading to the target property.