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My proposed research will develop new science and new computational tools for predicting how the structure of metals changes at microscopic length-scales. This will help us to control the properties of metals that we use in our everyday lives and to design new, improved metallic materials.
Most of the metallic materials on which we rely are polycrystals. They are made up of many grains in which the atoms are arranged in ordered patterns. The orientation of these patterns varies from one grain to the next and the surfaces between pairs of grains are known as grain boundaries.
The pattern of grain boundaries in a metal, its "grain structure", strongly affects the metal's behaviour, changing how it deforms, corrodes and cracks. If we can control the grain structure then we can control the metal's properties. We obtain a desired grain structure by subjecting a metal to mechanical processes and heat treatments, which make the grain boundaries move around. So controlling the grain structure requires the control of grain boundary migration.
My work uses computer simulations to understand why materials behave the way that they do. I have previously found that even simple grain boundaries move in different and complicated ways depending on conditions such as the temperature and how hard we push them to move. To predict the evolution of grain structures we need to approach the problem from the atomistic length-scale upwards. My proposed research will uncover the links between the shuffling of the individual atoms of metals with the evolution of their grain structure and so with their changing properties on the scale of our everyday lives. It starts at the level of fundamental physical laws (quantum mechanics, Newtonian dynamics) and produces results that matter to us all.
If we can understand how grain boundaries move then we can produce better metallic materials. That means stronger, lighter, cheaper, safer materials, with all the benefits to society that that implies.
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JOURNAL OF NUCLEAR MATERIALS (2024): 154828
Computational Materials Science (2023): 111985-111985
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