Materials scientists working at the cutting-edge of chemistry and physics need to drill down to the atomic level to properly understand the compounds they are dealing with. Materials scientists used to go straight to the lab to test out various plausible materials, now they start by modelling their prototype molecules on a supercomputer.
Dr Ravichandar Babarao from RMIT works on a whole field of interesting materials called Metal-Organic Frameworks (MOFs). By combining particular metals like zinc or cobalt with other carefully selected elements, he creates a lattice-shaped material with the capacity to store or filter large amounts of gases inside of its hollow pores.
The specific combination of elements and the shapes they are formed into determines exactly which gases get trapped most effectively. The applications of such a material are exciting and practical: CSIRO is hoping that MOFs will provide a cheap and effective way of capturing carbon dioxide from the air to produce dry ice, a compound used in many different industries. Other possible applications include filters inside of industrial gas masks, gas separation for the purification of complex gases and long-term carbon storage.
Conventional methods for doing these things are generally energy intensive and limited in scale. MOFs go beyond those traditional methods and allow simple and scalable solutions instead. But getting there requires detailed characterising of exactly what is going on inside the MOFs, and how the target molecules are interacting with them.
Quantum scale modelling of the atomic structure of MOFs on NCI’s supercomputer allows Dr Babarao to investigate their behaviour at different temperatures and pressures. This allows him to design new MOFs using the modelling data, rather than by experimental trial and error in the lab. There are already more than 80,000 different MOF structures outlined in a global research database; you need a high-throughput screening method to look through all of them for the properties you want.
Materials science developments to improve our future industrial and technological processes require large-scale high-performance computing facilities. They allow us to move beyond the typical processes of experimental synthesis, testing and validation. By understanding the fundamental behaviours of our future materials before we get to the complexity of physical production, we save time, produce better results and open up new possibilities that we may never have otherwise considered.
This research highlight was originally published in the 2018-2019 NCI Annual Report.
