The configuration in which the polymer chains are entangled determines the macroscale properties of the material. Use of machine learning for understanding soft matter (e.g. rubbers, plastics) has lagged behind hard matter because it is more challenging to define descriptors for a disordered system and multiscale system.

Our group has implemented a method for topologically identifying entanglement points in small systems. This project focuses on extending this method so that it can be applied to large polymer systems. This project will include scaling analysis (in terms of system size and system complexity), optimisation, periodic boundary conditions, analysis of a trajectory and visualisation.      

If taken as a 24 credit project scope could be extended to analyse non-linear or heterogeneous chains and/or trajectories of deformation processes. 

Goals: Code analysis and optimisation. Use software design concepts for implementation. Generate robust test cases.     

Keywords: Optimisation, feature generation, topology, software design, polymers 

Experience: Python and/or C required

Background literature

https://pubs.acs.org/doi/pdf/10.1021/ma062457k