Speaker: Trevor David Rhone, Assistant Professor at Rensselaer Polytechnic Institute
When the dimensionality of an electron system is reduced from three dimensions to two dimensions, new behavior emerges. This has been demonstrated in two-dimensional (2D) materials such as graphene – a single atomic layer of graphite – which was discovered in 2004. Many years later, in 2017, 2D materials with intrinsic magnetic order were discovered, giving rise to a new frontier in science exploration and industrial innovation. However many challenges in the search for new 2D magnetic materials exist. Some estimates place the number of materials that exist in nature as large as 10100. Is it possible to efficiently explore this vast chemical space in order to accelerate the discovery of 2D magnetic materials? Can we predict their properties?
In this talk we will use materials informatics – research at the intersection of materials science and artificial intelligence (AI) – to search for novel 2D magnets. In particular, we will exploit AI’s ability to learn materials’ representations to accelerate materials discovery and knowledge discovery. That is, we will harness AI to create physical insight into the microscopic origin of magnetic ordering in 2D materials. These materials have the potential to lead advances in data
storage, spintronics and quantum information.
Speaker Bio: Trevor David Rhone received a liberal arts education from Macalester College in Saint Paul. He went on to pursue his doctoral studies at Columbia University in the city of New York where he did experimental studies of two-dimensional electron systems in the extreme quantum limit. Trevor David spent several years at NTT Basic research laboratories in Japan. During a research stint at the National Institute of Materials Science in Japan, he transitioned to materials informatics research - exploiting machine learning tools to perform materials research. He continued this work at Harvard University where he used machine learning tools to search for new 2D magnetic materials.
Dr. Rhone's research interests involve using machine learning tools for materials discovery and knowledge discovery. Materials discovery could manifest in the search new 2D materials with exotic properties, the prediction of the outcome of industrially relevant catalytic reactions or for other compelling research problems. In addition, data analytics tools will be used to aid in developing a better understanding of physical systems.