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<p>Two-dimensional (2D) transition metal oxyhalides and nitrogen-halides (<i>T</i><sub><i>M</i></sub><i>BXs</i>, where <i>T</i><sub><i>M</i></sub> = transition metal, <i>B</i> = O-group and N-group elements, <i>X</i> = halogen) have emerged as promising candidates for exploring multiferroic orders and spintronic applications. In this study, we conduct a systematic first-principles high-throughput screening combined with machine learning to identify novel 2D ferromagnetic and multiferroic materials within <i>T</i><sub><i>M</i></sub><i>BX</i> family. From a comprehensive dataset comprising 672 <i>T</i><sub><i>M</i></sub><i>BX</i> monolayers, we identify 78 ferromagnetic systems, of which 38 exhibit high Curie temperatures (<i>T</i><sub>C</sub> ≥ 200 K), significantly expanding the known library of 2D magnetic materials. A machine learning model is developed to elucidate the key factors governing ferromagnetism, revealing that the second-nearest neighbor exchange interaction (<i>J</i><sub><i>2</i></sub>) plays a dominant role in determining <i>T</i><sub>C</sub>. Furthermore, we discovered seven ferromagnetic-ferroelectric multiferroic systems, revealing unique polarization switching pathways. Notably, spin transport simulations using the nonequilibrium Green’s function formalism demonstrate exceptional spin filtering capabilities (~100%) and giant bias-dependent tunneling magnetoresistance (&gt;10<sup>5</sup>%). These findings deepen the fundamental understanding of 2D multiferroics and establish a desired platform for future experimental exploration and the development of next-generation spintronic devices.</p>

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High throughput discovery of 2D ferromagnetic and multiferroic transition metal oxyhalides and nitrogen halides

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月10日

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  • Shaowen Xu,
  • Fanhao Jia,
  • Ning Dai

摘要

Two-dimensional (2D) transition metal oxyhalides and nitrogen-halides (TMBXs, where TM = transition metal, B = O-group and N-group elements, X = halogen) have emerged as promising candidates for exploring multiferroic orders and spintronic applications. In this study, we conduct a systematic first-principles high-throughput screening combined with machine learning to identify novel 2D ferromagnetic and multiferroic materials within TMBX family. From a comprehensive dataset comprising 672 TMBX monolayers, we identify 78 ferromagnetic systems, of which 38 exhibit high Curie temperatures (TC ≥ 200 K), significantly expanding the known library of 2D magnetic materials. A machine learning model is developed to elucidate the key factors governing ferromagnetism, revealing that the second-nearest neighbor exchange interaction (J2) plays a dominant role in determining TC. Furthermore, we discovered seven ferromagnetic-ferroelectric multiferroic systems, revealing unique polarization switching pathways. Notably, spin transport simulations using the nonequilibrium Green’s function formalism demonstrate exceptional spin filtering capabilities (~100%) and giant bias-dependent tunneling magnetoresistance (>105%). These findings deepen the fundamental understanding of 2D multiferroics and establish a desired platform for future experimental exploration and the development of next-generation spintronic devices.