Unlocking the mystery of materials ...
COMPUTATIONAL MATERIALS ANALYSIS LAB
Welcome to the Computational Materials Analysis Lab (CMALab). The discovery and development of cheaper, lighter yet stronger structural materials is paramount to maintain the steady technological progress which has impacted positively on the way we live. It is increasingly recognized that theory and computation play a key role in materials discovery. From the point of view of scientific investigations, one of the great strengths of computer simulations over physical experiments is the ability to study a complete range of physical and temporal scales. That is, we can study in detail physical phenomena that last only a picosecond, or we can move back in time and identify the evolution of interesting physical events. The core research focus of CMALab is on the special challenge of multi-scale simulations, which are essential for engineering systems containing nanoscale constituents.
Materials yet to be discovered may well not be represented in existing databases. Unbiased microstructure prediction techniques are currently being developed, which allows for the computational exploration of complex metals and alloys, and the identification of non-equilibrium structures forming during processing e.g additive manufacturing, casting, heat treatment etc. Integration of a phase-field, CALPHAD and machine learning algorithms is being done to create a fast-acting ICME tool for the analysis of complex liquid-solid and solid-solid phase transformations.
Prediction of mechanical response
With the rapid development in aviation and energy sectors amongst others, flourishing research to manufacture materials that exhibit long service life and reliable is highly in demand. With the constraint of cost and time, modeling of alloys becomes priority to study the material response in extreme conditions of high stress and temperature. Continuum crystal plasticity has become the appropriate framework to simulate the deformation of metallic specimens under complex loading conditions (tension, shear, torsion, etc.), but also components like turbine blades in jet engines. Efforts are ongoing in utilizing the crystal plasticity-based finite-element method to model the mechanical response of various crystalline materials.
Data analysis and machine learning
Along with imaging, computation is a source of “big data” in materials science. Tools are being developed to rationalize and visualize this wealth of data. Machine learning techniques, trained on this computationally derived data, are being used to make predictions of materials properties and/or suggest new materials