When zoomed in on the nano or micronscale, the natural world is almost unbelievably beautiful: an amazingly rich collection of natural building blocks move collectively to give both structure and function to our world. This self-organization is responsible for diverse phenomena 鈥� from the amazing structural colors exhibited in gem opals or in the wings of Morpho butterflies, to the complex dance carried out in cells that allows life to flourish. Mimicking these self-assembly processes to create new materials, designed to exhibit desired and targeted material properties is arguably the holy grail of materials science and a key ingredient in creating the materials that will help us in realizing a sustainable future.
Computer simulations play a key role in this quest: allowing us to study self-assembly processes and giving us insights into how the interactions between the building blocks of a material 鈥� whether atoms, molecules, proteins, or colloidal particles 鈥� give rise to the final structure.
My research group uses, and develops, a combination of state-of-the-art computational and machine learning algorithms to study self-assembly in soft matter systems 鈥� both in and out of equilibrium. We have made significant contributions to our understanding of how defects manifest in colloidal crystals, crystal nucleation, the relationship between structure and dynamics in supercooled liquids, and self-assembly behaviour in active colloids.
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