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Magnetic skyrmions, characterized by their topological properties, serve as core components for developing next-generation non-volatile memory devices that demand high density, high speed, and low power consumption. Their formation arises from the Dzyaloshinskii-Moriya interaction (DMI), enabled by noncentrosymmetric structures. Two-dimensional Janus magnetic materials, which inherently break spatial inversion symmetry, readily generate strong DMI, providing an ideal platform for skyrmion generation and novel racetrack memory applications. Within this field, identifying systems with a high Curie temperature (TC) is crucial, as it directly governs magnetic property stability and application potential under high-temperature conditions. This study integrated literature and open-source databases to construct a dataset of 16,880 ABC-type two-dimensional materials. Utilizing stoichiometric ratios, intrinsic elemental properties, and electronic structure features as descriptors, four machine learning models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Extra Trees (ET)—were employed for TC prediction. Model performance was evaluated via ten-fold cross-validation, revealing that the XGBoost model exhibited superior prediction accuracy and generalization capability. Leveraging this model, TC was predicted for 4,024 unexplored two-dimensional Janus materials. This screening identified 54 promising candidates possessing thermal stability, high magnetic moment, and a TC exceeding 300 K. To verify reliability, four candidate systems (EuFeO, GdKTi, DyFeTb, ErFeGd) were randomly selected for theoretical validation using first-principles calculations combined with the Heisenberg model. For systems exhibiting strong correlation effects (containing d-orbital electrons), the Hubbard U parameter was incorporated to describe on-site Coulomb repulsion. Exchange coupling constants were derived using the VASP software package. Subsequently, TC values were calculated via classical Monte Carlo simulations performed using the MCSOLVER program. Results demonstrate that the mean absolute error (MAE) of the predicted TC agrees well with the model calculations for EuFeO and GdKTi, while larger deviations were observed for DyFeTb and ErFeGd. Nevertheless, the calculated TC values for all four candidates surpass room temperature. This work establishes a new computational framework for the effcient screening of high-performance two-dimensional Janus magnetic materials, contributing to the advancement of magnetic storage technologies.
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Keywords:
- Machine Learning /
- two-dimensional magnetic Janus materials /
- Curie temperature /
- firstprinciples calculations
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[1] Novoselov K S, Geim A K, Morozov S V, Jiang D, Zhang Y, Dubonos S V, Grigorieva I V, Firsov A A 2004 Science 306 666
[2] Zhang Z W, Lang Y F, Zhu H P, Li B, Zhao Y Q, Wei B, Zhou W X 2024 Phys. Rev. Appl. 21 064012
[3] Liu B, Feng X X, Long M Q, Cai M Q, Yang J L 2022 Phys. Rev. Appl. 18 054036
[4] Xiong X J, Zhong F, Zhang Z W, Chen F, Luo J L, Zhao Y Q, Zhu H P, Jiang S L 2024 Acta Phys. Sin. 73 137101
[5] Zhao Y Q, Liu Z S, Nie G Z, Zhu Z H, Chai Y F, Wang J N, Cai M Q, Jiang S L 2021 Appl. Phys. Lett. 118 173104
[6] Lang Y F, Zou D F, Xu Y, Jiang S L, Zhao Y Q, Ang Y S 2024 Appl. Phys. Lett. 124 052903
[7] Liao C S, Ding Y F, Zhao Y Q, Cai M Q 2021 Appl. Phys. Lett. 119 182903
[8] Tan W, Zhang Z W, Zhou X Y, Yu Z L, Zhao Y Q, Jiang S L, Ang Y S 2024 Phys. Rev. Mater. 8 094414
[9] Lu X, Fei R, Yang L 2019 PHYSICAL REVIEW B 100
[10] Jiang Z, Wang P, Jiang X, Zhao J 2018 NANOSCALE HORIZONS 3 335
[11] Sanvito S, Oses C, Xue J, Tiwari A, Zic M, Archer T, Tozman P, Venkatesan M, Coey M, Curtarolo S 2017 SCIENCE ADVANCES 3
[12] Zhang S Q, Xu R Z, Luo N N, Zou X L 2021 Nanoscale 13 1398
[13] Dai C Y, He P, Luo L X, Zhan P X, Guan B, Zheng J 2023 Sci. China Mater. 66 859
[14] Wang P, Zong Y X, Wen H Y, Xia J B, Wei Z M 2021 Acta Phys. Sin. 70 026801
[15] Ren K, Wang K, Zhang G 2022 ACS Appl. Electron. Mater. 4 4507
[16] Peng Z L, Huang J X, Guo Z G 2021 Nanoscale 13 18839
[17] Zhang L, Yang Z J F, Gong T, Pan R K, Wang H D, Guo Z N, Zhang H, Fu X 2020 J. Mater. Chem. A 8 8813
[18] Vafaeezadeh M, Thiel W R 2022 Angew. Chem. Int. Edit. 61 e202206403
[19] Mukherjee T, Kar S, Ray S 2022 J. Mater. Res. 37 3418
[20] Li C Q, An Y K 2022 Phys. Rev. B 106 115417
[21] Zhang L, Zhao Y, Liu Y Q, Gao G Y 2023 Nanoscale 15 18910
[22] Xu L J, Wan W H, Peng Y R, Ge Y F, Liu Y 2024 Ann. Phys. 536 2300388
[23] Gao Z Y, Mao G Y, Chen S Y, Bai Y, Gao P, Wu C C, Gates I D, Yang W J, Ding X L, Yao J X 2022 Phys. Chem. Chem. Phys. 24 3460
[24] Liu H, Sun J T, Liu M, Meng S 2018 J. Phys. Chem. Lett. 9 6709
[25] Nelson J, Sanvito S 2019 Phys. Rev. Mater. 3 104405
[26] Belot J F, Taufour V, Sanvito S, Hart G L 2023 Appl. Phys. Lett. 123 042405
[27] Miyazato I, Tanaka Y, Takahashi K 2018 J. Phys.: Condens. Matter 30 06LT01
[28] Lu S H, Zhou Q H, Guo Y L, Zhang Y H, Wu Y L, Wang J L 2020 Adv. Mater. 32 2002658
[29] Ma X Y, Lyu H Y, Hao K R, Zhao Y M, Qian X F, Yan Q B, Su G 2021 Sci. Bull. 66 233
[30] Huang T, Yang Z X, Li L, Wan H, Leng C, Huang G F, Hu W Y, Huang W Q 2024 J. Phys. chem. Lett. 15 2428
[31] Chaney G, Ibrahim A, Ersan F, Çakır D, Ataca C 2021 ACS Appl. Mater. Interfaces 13 36388
[32] Yan X H, Zheng J M, Zhao X, Zhao P J, Guo P, Jiang Z Y 2024 Phys. Status Solidi Rapid Res. Lett. 18 2300468
[33] Xin C, Yin Y, Song B, Fan Z, Song Y, Pan F 2023 Chip 2 100071
[34] Jung S G, Jung G, Cole J M 2024 JOURNAL OF CHEMICAL INFORMATION AND MODELING 64 6388
[35] Qiao Z, Wei T, Ning Y Q, Nie G Z, Cai M Q, Wang J N, Zhu H P, Zhao Y Q 2024 ACTA PHYSICA SINICA 73
[36] Ward L, Dunn A, Faghaninia A, Zimmermann N E, Bajaj S, Wang Q, Montoya J, Chen J, Bystrom K, Dylla M, Chard K, Asta M, Persson K A, Snyder G J, Foster I, Jain A 2018 Comp. Mater. Sci. 152 60
[37] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E 2011 J. Mach. Learn. Res. 12 2825
[38] Ester M, Kriegel H P, Xu X 2023 Geogr. Anal. 55 207
[39] Wu J, Chen X Y, Zhang H, Xiong L D, Lei H, Deng S H 2019 J. Electron. Sci. Technol. 17 26
[40] Ma Q Y, Wan W H, Ge Y F, Li Y M, Liu Y 2022 J. Magn. Magn. Mater. 605 172314
[41] Yin W J, Tan H J, Ding P J, Wen B, Li X B, Teobaldi G, Liu L M 2021 Mater. Adv. 2 7543
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