Artificial intelligence to advance materials research
The Rhone research group is at the intersection of materials science and artificial intelligence. Machine learning tools are exploited to uncover hidden patterns in materials data and to predict new materials with novel properties for advances in science and industrial innovation.
High-throughput density functional theory (DFT) calculations are used to simulate the materials properties of quantum materials. The resulting data are used to create a database of materials properties that are used to perform screening for materials discovery and to train machine learning models.
We explore emergent phenomena in correlated quantum systems. In particular, we focus on spin properties and magnetic ordering of layered materials with intrinsic magnetic order.
Experimental probes are used to confirm both AI predictions and first principles calculations of materials properties. In particular, Kerr spectroscopy is used to interrogate magnetic order of van der Waals materials down to a single atomic layer.
Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute
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