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Collaborative Research: DMREF: Discovery of novel magnetic materials through pesudospin control (NSF)

Materials discovery requires new tools that enable design principles. Notable examples are the recent advances in topological materials due to insights from the interplay of topology and symmetry. Here we propose to develop pseudospin control as a materials design tool for the discovery new magnetic states and superconductivity. The concept of pseudospin describes the two-fold Kramers degeneracy of Bloch electrons that arises at each momentum point k when the product of time-reversal T and inversion I symmetries is present. Normally, this pseudospin behaves as spin-1/2 under rotations, which drives much of our understanding of quantum materials, including Cooper pairing in superconductors, Stoner ferromagnetism, and the control of spin by Zeeman fields. Our recent work shows, however, for crystals with non-symmorphic space symmetry, the pseudospin can behave very differently than the usually spin-1/2, which can drive novel magnetic states, including altermagnets and odd-parity multipole magnets, and qualitatively alters the superconducting response to magnetic fields.

This project will utilize pseudospin control as a new paradigm for materials discovery, following the collaborative and iterative closed-loop approach outlined in the MGI strategic plan, by combining analytic and predictive computational theory with experimental molecular beam epitaxy (MBE) growth and characterization with in situ scanning tunneling microscopy/spectroscopy (STM/S) and angle-resolved photoemission spectroscopy (ARPES), as well as ex-situ magneto-optical and electrical transport measurements.

Towards high-temperature topological superconductivity – Nodal superconductors with a twist (DOE)

Current quantum computers process information using quantum bits, or qubits, based on fragile, short-lived quantum mechanical states. This project aims to develop new material systems that make qubits topological, thereby making them robust and capable of operating at higher temperatures. Specifically, we focus on topological superconductors (TSCs), one class of topological materials that host Majorana zero modes (MZMs), which can be used as qubits for topological quantum computation. Common approaches to TSCs are based on proximitized superconductivity in nanostructures with strong spin-orbit coupling or by interfacing a BCS superconductor with magnetism. While there have been reports of MZMs, due to the low Tc of conventional BCS superconductors, the experimental confirmation of TSCs must be carried out at very low temperatures, and the evidence has been hotly debated.

We are exploring a new approach to creating TSCs in twisted structures by stacking or stitching two nodal superconductors with a predetermined twisting angle. Different from the current approach of fabrication by “tear and stack” mechanical assembly, we will synthesize the twisted structures by van der Waals epitaxial growth of layered superconductors, focusing on Fe-chalcogenides. We will investigate zero-dimensional (0D) MZMs at cores of screw dislocations in multilayer FeSeTe films and one-dimensional (1D) chiral edge modes in multi-twisted FeSeS spiral structures grown on cleaved cuprate and convex/concave graphene substrates. Density functional theory calculations will be carried out on the effects of twisting at the proposed heterostructures. Optimal structures will be synthesized by molecular beam epitaxy, and 0D MZMs and 1D chiral edge modes will be probed by scanning tunneling microscopy/spectroscopy (STM/S). These will be compared with generative and reinforcement machine learning (ML) modeling to differentiate topological and trivial modes. We aim to create ML models to better recognize hidden patterns in STM images and dI/dV tunneling spectra to distinguish between the MZMs and trivial states produced by defects/impurities.

Data-driven autonomous experiments for energy sciences: from first principles to machine learning (WV Higher Education Policy Commission)

Science paradigms have changed rapidly over the past several centuries. From empirical to theoretical (first principles) and computational (e.g., density functional theory (DFT)), we have witnessed the emerging data-driven science in which artificial intelligence (AI) and machine learning (ML) are becoming indispensable tools in physical sciences. Various experiments, such as molecular dynamics and chemical reactions, can be made “autonomous” by developing data-driven surrogate models that are more time- and cost-effective than their physical implementations. However, rapid advances in AI/ML have offered niche opportunities for next-generation data-driven models with even better performance. Due to the culture barrier, there is still a significant gap between domain knowledge dictated by first principles and surrogate models developed by ML experts. To fill this gap, we propose to expand convergence research at the intersection of AI/ML and energy sciences by developing a class of novel physics-informed surrogate models, including contrastive learning, self-supervised learning, and graph learning.

Optically controlled quantum phase transitions at van der Waals interfaces (DOE)

As alluded to in the DOE report on the “Basic Research Needs for Synthesis Science”, the advancement of human civilization is marked by signature materials. While the current Si-based information technologies are fast approaching physical limits set by dissipation, density, and speed, quantum materials are poised to become the new generation of materials that will lay the foundation for the emergent “Quantum Age”, as recognized by the National Quantum Initiative Act. Since quantum materials derive their properties from the interplay of symmetry, topology, reduced dimensionality, and strong correlations, the objective of our proposed research is to understand and control these parameters through the innovative design of epitaxial quantum material heterostructures, implementation of novel optical excitations, and atomic-scale lattice tracing using cutting edge synchrotron tools. Specifically, this project will focus on the Van der Waals (VdW) interfaces formed from two-dimensional (2D) superconductors coupled to correlated complex oxides or to polar 2D semiconductors, and their responses to tailored light fields.