Symmetry-guided data-driven discovery of native quantum defects in two-dimensional materials
Jeng-Yuan Tsai, Weiyi Gong, Qimin Yan

TL;DR
This paper introduces a symmetry-guided, data-driven workflow to identify and evaluate native quantum defects in 2D materials, highlighting antisite defects in PTMCs as promising quantum defect platforms for quantum technologies.
Contribution
It develops a comprehensive symmetry-based method combined with high-throughput computations to discover and analyze quantum defects in 2D materials, expanding the known defect landscape.
Findings
Antisite defects in diverse 2D hosts are promising quantum defects.
16 antisites in PTMCs are identified as key quantum defect platforms.
High-throughput calculations estimate their magneto-optical properties.
Abstract
Drawing on their atomically thin structure, two-dimensional (2D) materials present a groundbreaking avenue for the precision fabrication and systematic manipulation of quantum defects. Through a method grounded in site-symmetry principles, we devise a comprehensive workflow to pinpoint potential native quantum defects across the entire spectrum of known binary 2D materials. Leveraging both symmetry principles and data-driven approaches markedly enhances the identification of spin defects exhibiting triplet ground states. This advancement is pivotal in discovering NV-like quantum defects in 2D materials, which are instrumental in facilitating a set of quantum functionalities. For discerning the multifaceted functionalities of these quantum defect candidates, their magneto-optical properties are comprehensively estimated using high-throughput computations. Our findings underscore that…
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Taxonomy
TopicsMachine Learning in Materials Science
