Research at materials intelligence

Emergent phenomena in quantum materials

More is different. A collection of particles is more than the sum of its individual components. For instance, a collection of electronic spins can give rise to ferromagnetic order where all the spin align. Our research aims to predict the behavior of emergent phenomena in materials. We use a wide range of techniques to tackle this very challenging problem: (i) First-principles calculations, (ii) Quantum computing, (iii) Light scattering experiments, and (iv) Artificial intelligence.

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.

Quantum computing simulations

We leverage RPI's 127-qubit quantum computer to simulate the properties of materials. We focus on materials systems that are challenging to simulate using classical computers, such as strongly correlated electronic systems.

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.

Experiments

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

Rhone Research Group