Rensselaer Polytechnic Institute, 110 8th St, Troy, New York 12180, United States
RESEARCH
Emergent properties in quantum materials
Our work seeks to elucidate emergent phenomena in quantum materials, with a focus on the spin and charge degrees of freedom of electron systems in reduced dimensions. We exploit artificial intelligence (AI) to discover novel materials (especially two-dimensional (2D) materials) and to create physical insight. The materials intelligence we are constructing will guide the search for materials that exhibit magnetic order, topological order and superconductivity. This research has applications in spintronics, data storage, biosensing, catalysis, quantum computing and quantum communication.
Two-dimensional (2D) materials with intrinsic magnetic order are a platform for studying exotic spin degrees of freedom in reduced dimensions. Novel spin phenomena in van der Waals (vdW) materials and their heterostructures are at the forefront of condensed-matter-physics research. In particular, new behavior may emerge from an interplay between magnetic and topological order in vdW heterostructures. It is well known that topological states emerge when electron spins are confined to two dimensions. When two or more distinct monolayers are combined into a heterostructure, surprising behavior may arise, such as enhanced topological order or the formation of spin textures. An estimate for the total number of vdW materials, multilayers and heterostructures is ~10^21. This vast search space is overwhelming for first-principles calculations and experimental probes alone. The project combines density functional theory calculations, quantum computer simulations and new artificial intelligence (AI) tools to facilitate efficient navigation through this large materials space. The goals are to discover novel materials and to gain physical insight into spin properties and emerging phenomena of vdW materials at the electronic level. This insight will pave the way to a fundamental understanding of how crystal structure influences quantum properties in materials.
Advances in materials drive technological innovation. Ultimately, we will apply quantum computer simulations, classical computer simulations and AI tools to accelerate the discovery of materials – with a focus on 2D magnets – that are suitable for spintronics, data storage and novel quantum computing architectures.
Collaborations
Dr. Yoshiharu Krockenberger
Prof. Humberto Terrones
First-principles calculations of two-dimensional materials