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    Controllable Quantum Phase Transitions and Frustrated Spin Responses in Multiferroic FeS Nanotubes

    Chi Ding*, Tao Yang*, Qing Lu, Yu Han, Yijie Zhu, Junjie Wang, Rui Wang†, Hui-Tian Wang, Dingyu Xing et al.

    Jian Sun‡

    • National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China

    • *These authors contributed equally to this work.
    • †Contact author: rwang89@nju.edu.cn
    • ‡Contact author: jiansun@nju.edu.cn

    Phys. Rev. Lett. 137, 116402 – Published 11 September, 2026

    DOI: https://doi.org/10.1103/xf8k-4r35

    Abstract

    Geometric frustration drives exotic quantum phases and transitions in condensed matter. One-dimensional frustrated magnets with inherent controllability are particularly promising for studying fundamental quantum physics, yet experimentally tunable systems remain scarce. Here, using first-principles calculations and confined structure search, we predict a novel one-dimensional platform: confined FeS nanotubes with twisted-triangular geometry. These nanotubes exhibit a triple-well energy landscape, hosting two ferroelectric and one paraelectric phases, exhibiting collinear antiferromagnetic and 120° noncollinear (NCL) orders, respectively. Application of axial strains tunes magnetic interactions and frustration, driving transitions from antiferromagnetic to NCL, and then to a double-cell NCL phase. The phase diagram can be described by a model with two dimensionless parameters. Large-scale tensor network simulations reveal starkly distinct chirality responses and magnetization plateaus under different magnitudes of magnetic fields for each phase, directly linking these behaviors to tunable frustration. Therefore, the multiferroic FeS nanotube provides a versatile platform for exploring electrically tunable quantum states, phase transitions, and field-induced phenomena associated with evolving magnetic frustration. This Letter establishes a general strategy combining first-principles prediction, confined structure search, and tensor networks for discovering and understanding frustrated low-dimensional quantum phenomena.

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