Rensselaer Polytechnic Institute, 110 8th St, Troy, New York 12180, United States
Rhone Research Group
Artificial intelligence to advance materials research
Rhone Group at a glance
Artificial intelligence for materials
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 DFT calculations
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.
Quantum Materials
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.
Materials characterization
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.
Materials Intelligence
Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute
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