CAREER: EMERGENT BEHAVIOR OF VAN DER WAALS MAGNETIC MATERIALS: THE GENESIS OF MATERIALS INTELLIGENCE

Rensselaer Polytechnic Institute, Troy NY

Investigators

Abstract

NON-TECHNICAL SUMMARY This project applies quantum calculations and artificial intelligence to the understanding and discovery of new van der Waals (vdW) materials, atomically thin sheets that can be stacked with weak bonds between layers. Graphite is a familiar example. We now know how to isolate and measure one, two, or any number of such sheets in controllable combinations. Discovered only in 2017, magnetic vdW layers will become the building blocks for novel quantum materials with numerous potential technological applications, starting with data storage. In the near future, these materials may give engineers control over the quantum of magnetism, called spin, leading to additional applications in the nascent field of two-dimensional spintronics. They may also exhibit "topological" order, which is robust against impurities and imperfections, making them suitable for applications in quantum computers. The magnetic properties of vdW (i.e. two-dimensional) materials are different from those of bulk (three-dimensional) materials, creating new avenues for physics exploration. The chemical composition of monolayers, multilayers and heterostructures (stacks of different types of layers) of vdW magnets can be tuned to design novel materials with desirable and potentially surprising properties. The resulting number of candidate vdW monolayers, multilayers and their heterostructures is combinatorially large, with estimates far exceeding trillions of vdW materials. Consequently, inspecting the entire set of possible combinations using experiments or direct computational simulations is impossible. Nevertheless, the challenge of searching for new materials with desirable properties can be mitigated with the help of artificial intelligence (AI). This project will leverage AI to search for new vdW materials with novel spin and topological properties. In addition, AI will be harnessed to provide physical insight into the microscopic origins of spin order and topological order in these materials. This interdisciplinary research will train scientists at different stages of their careers to apply AI tools to their own research. The educational component of the project will focus on teaching and mentoring undergraduate and graduate students through carefully designed coursework and hands-on research experiences. In addition, students will learn effective science communication and share their research via public lectures and webinars. The PI plans to bring students together for positive interactions and create a mentor network to address the needs of students. In addition, students will learn effective science communication and share their research via public lectures and webinars. The activities will provide a supportive environment that will enable students to achieve. TECHNICAL SUMMARY 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 with 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 magnetic properties in 2D materials, including monolayers, multilayers and heterostructures. The AI architectures the research team develops will find use in other areas of condensed matter physics and the broader scientific community. The research is interdisciplinary and will connect scientists and topics that span diverse areas. Concepts from physics may enable advances in AI and vice versa. Furthermore, the 2D materials discovered through this work will provide experimentalists with a materials playground in which to study novel and emergent spin properties. In addition, the proposed research will have potential impact in industry - e.g., data storage and other spintronics applications, facilitating future memory technologies. This research may also help create advances in future computer architectures, such as those for quantum computing. This project will train scientists at different stages of their careers to apply AI tools to their own research. The educational component focuses on teaching and mentoring undergraduate and graduate students through carefully designed coursework and hands-on research experiences. The PI plans to bring students together for positive interactions and create a mentor network to address the needs of students. In addition, students will learn effective science communication and share their research via public lectures and webinars. The activities will provide a supportive environment that will enable students to achieve. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.