Implementation of entanglement analysis to obtain topological descriptors


External Member

Amanda Parker (Primary Supervisor), School of Computing


Use of machine learning for understanding soft matter has lagged behind hard matter because it is more challenging to define descriptors for a disordered system that for an ordered one (eg. a lattice). This project will focus on describing the topology of a dense polymer material. Polymers chains cannot move freely through other chains, this constrains every chain’s movement. The configuration in which the polymer chains are entangled determines the macroscale properties of the material (e.g. is it a rubber or a plastic). This project will implement the Kroger-Z1 method in Python to identify entanglement points for each chain in a dense polymer material. These entanglement points can move or disappear as chains rearrange around each other. 

The Primary Supervisor for this project is Dr Amanda Parker, who can be contacted at


Data management. Use software design concepts for implementation. Generate robust and increasingly complex test cases.      


Python programming and experience in data science and machine learning is essential (such as COMP3720, COMP4660, COMP4670, COMP6670, COMP8420).  Familiarity with platforms such as scikit-learn is desirable.


This can be eitehr a 12cp or 24cp project.  If taken as a 24 credit project the original method can be extended to analyse non-linear or heterogeneous chains and/or trajectories of configurations.  


Feature generation, topology, software design, polymers  

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