Designing Advanced Materials for Industry
Materials scientists have long aimed to make use of the microstructure patterning that takes place in matter during non-equilibrium phase transformations. (In physics, “equilibrium” refers to a state where objects are not in motion, so have no energy flowing through them. In the real world, most objects are moving or causing others to move, so are in a non-equilibrium state.) This is the quintessential feature that controls the properties of real-world materials.
Computational modelling holds enormous promise for producing predictive design tools that can make this happen. It provides a cost-effective channel through which scientists can explore how to improve or find new microstructure-property relationships in manufactured products. This is particularly true in modern additive manufacturing processes.
Dr. Nikolas Provatas, Canada Research Chair in Computational Materials Science, is developing a comprehensive, multi-scale modelling chain to predict the evolution of microstructures in materials across multiple scales. The chain will consolidate the hybrid atomic-continuum theories he has recently developed into a single quantitative framework and extract the behaviour of such models beyond the nano-micron scales and on to scales that are relevant to real-world applications. At the same time, Provatas and his research team are developing novel multi-scale and machine learning techniques to achieve the highly scalable computation of these models.
This work is particularly important for materials engineers. Industry is always looking for processing routes that can help them achieve targeted properties in materials more cost-effectively. Ultimately, the tools Provatas is developing will change the way we understand and predictively design advanced materials from the nano-micron scales up.