Emergent properties in quantum materials
We investigate emergent phenomena in quantum materials, with a focus on the spin and charge degrees of freedom of electron systems in reduced dimensions. We leverage artificial intelligence to discover novel materials — particularly two-dimensional (2D) materials — and to extract physical insight from the data those materials produce.
What we're chasing
Magnetic order, topological order, and superconductivity in 2D materials, and the exotic spin phenomena that emerge in van der Waals heterostructures. These effects have direct applications in spintronics, data storage, biosensing, catalysis, quantum computing, and quantum communication.
How we work
We combine density functional theory calculations, quantum computer simulations, and AI tools to navigate an estimated 1021 possible van der Waals materials and heterostructures — a search space far beyond what first-principles calculations alone can cover.
Six connected areas of focus
Emergent phenomena
Collective behavior in many-body electron systems, probed with first-principles calculations, quantum computing, light scattering, and AI.
Quantum materials
Correlated quantum systems, with a focus on magnetism in layered, van der Waals materials.
Quantum computing
Using RPI's 127-qubit quantum computer to simulate materials properties that challenge classical methods.
AI for materials science
Machine learning to find patterns in materials data and predict new materials with desired properties.
High-throughput DFT
Large-scale density functional theory calculations that build materials-property databases for screening and model training.
Experimental validation
Techniques such as Kerr spectroscopy to validate theoretical predictions at atomic scales.
Working with the community
Our work benefits from close collaboration across institutions, including Dr. Yoshiharu Krockenberger (Materionics) and Prof. Humberto Terrones, whose first-principles calculations of 2D materials complement our data-driven approach.