Only a few atoms thick, 2D materials are one of the hottest topics in physics
Since the discovery of the supermaterial graphene, for which two scientists were awarded a Nobel prize in 2010, hundreds of other 2D materials have been identified. These have either been made experimentally or identified through computational modelling.
2D materials have promising applications in electronics, healthcare and energy materials. To utilise them, we first need to understand their specific quantum properties, which require advanced scientific facilities and techniques.
Data analysis in rapid turnaround simulations
Exposing 2D materials to the powerful X-rays produced at a synchrotron beam facility is one of the most effective ways of studying electron behaviour in the materials. The process generates vast amounts of data that can be analysed using sophisticated cloud computational modelling.
Typically, our experiments generate vast volumes of new data in short intensive experiments rather than in a steady well-paced flow. Newly produced data has to be analysed in rapid turnaround simulations that most often can’t be achieved with a university’s limited on-site supercomputing resources.
Valuable research experience for students
Another challenge of our research area is to make it accessible and engaging for undergraduate students.
Apart from a few textbook calculations, there is little that students can contribute to our modelling work without using the same expensive computational tools as us.
Inspired by astrophysics, where undergraduates are regularly given observational data to analyse, we wanted to explore the ability of cloud computing to deliver accelerated student training in our methodologies before giving them real scientific problems to solve.
Optimal computational resources
Cloud supercomputing resources have allowed us to carry out computational work at a range of scales with widely varying numbers of cores and compute times, optimised for the requirements of our experiments. This flexibility meant we could run calculations at the optimum core numbers for given parallelisation strategies, making our calculations more efficient in terms of total core-hours used. At the same time, it has also improved turnaround time.
Our undergraduate students can now receive faster and improved critical training. For example, a well-converged scientific calculation of publishable quality requires the right balance between precision and costs, but doing this can be hindered by limits on computational resources.
The flexibility of cloud supercomputing solutions removed this limitation. It meant the work of producing scientific results and proving their reliability could be carried out by our students, allowing them to make a substantial research contribution.
Including undergraduate students in our work has given them valuable experience in conducting actual scientific research within an active and diverse research group. Our students have appreciated and been inspired by the resources they could access. This type of experience is vital in recruiting new researchers into the field.