Research interests involve using machine learning tools for materials discovery and knowledge discovery. Materials discovery could manifest in the search new two-dimensional (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.
We seek to discover 2D magnetic materials with novel properties for applications in spintronics, data storage and creating advances in fundamental science research. We exploit machine learning tools to uncover hidden patterns which will guide both materials discovery and knowledge discovery. Just as the Mendeleev Periodic table can be exploited for its chemical trends to understand chemistry and chemical properties, we will use AI to create a framework to identify trends in crystal structure and small molecules that can be exploited for advances in scientific understanding and industrial innovation.
Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute
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