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Software engineering, theory & modelling

We apply theoretical soft-matter physics to understand macroscopic phenomena in (biological) soft-matter at the molecular level, and use these insights to address global challenges in our key research themes. Our expertise in software engineering bridges the gap between molecular insights and macroscopic predictions while driving automation in Instrument Development, including using both deterministic and machine learning (ML) techniques to automate the capture, and analysis of, atomic force microscopy (AFM) images. We design bespoke software solutions tailored for precision and efficiency.

Example Projects include:

Using deterministic and machine learning (ML)  techniques to improve the reliability and throughput for AFM experiments investigating the properties of materials, from the single atom to micron length scale.

We are theoretically studying how ‘intrinsic disorder’ of molecular building blocks affects self-assembly, network formation, rheology, and liquid-liquid phase separation.

We are developing models and algorithms that couple the dynamics of topological (protein) networks to the stochastic physics by which crosslinks are formed or broken, and by which local stresses couple to the folding/unfolding of proteins.

We  are advancing our models by applying it to understand the process by which silkworms spin silk fibres. This natural exemplar tests our models in ‘extreme’ (i.e. strongly non-linear) conditions where the networks are subjected to large external shear forces.

Academics working in this area: