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My name is Peyman Saidi and I am working with Prof. I. Tamblyn on applying machine learning algorithms to materials engineering problems. I am a research associate in the nuclear materials research group of Prof. Mark Daymond at Queen's University.
My research interests are in elucidating the fundamental mechanisms of microstructural evolution for systems in materials science, in order to better predict the behaviour of engineering materials, through the development and application of multi-scale numerical, computational and phenomenological models. The long term objective of my research is to bridge the gap between the mesoscale description of microstructural elements employed in materials science and the information gathered from simulations.
In this group we are collaborating to develop deep learning methods which can rapidly approximate the properties of materials based on modelling training data, and leverage Machine Learning tools to perform physics-based modelling of materials.
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