Symmetry-Guided and Data-Driven Discovery of Quantum Defects in Two-Dimensional Materials

Being atomically thin and amenable to external controls, two-dimensional (2D) materials offer a new paradigm for the realization of qubit fabrication and operation at room temperature for quantum information sciences and technologies. In this talk, as an example of quantum material design by local bonding symmetry, I will discuss how data-driven approaches (high-throughput computations and machine learning) can be combined with symmetry-based physical principles to guide the search for quantum defects in 2D materials for quantum technologies. In our initial work, the use of site symmetry (irreducible representations) as a material design hypothesis enables the identification of anion antisite defects as promising spin qubits and quantum emitters in six monolayer transition metal dichalcogenides. The expansion of the high-throughput search in all known binary 2D materials led to the identification of a large amount of antisite and substitutional defect candidates in multiple families of 2D systems that can be utilized as qubits, quantum sensors, and/or quantum emitters. Furthermore, we will demonstrate how machine learning can effectively accelerate the discovery process by constructing models that are specially designed for local defect properties such as formation energetics and spin-related properties. This work serves as an initial step toward the construction of a technically accessible 2D platform for the fabrication of novel spin-defect-based systems for quantum technologies.