-
Discovering the compact、 stable and easily controllable nanoscale non-trivial topological magnetic structures—magnetic skyrmions,is the key to develop next-generation high-density, high-speed,and lowenergy non-volatile information storage devices.Based on the topological generation mechanism,magnetic skyrmions could be generated through the Dzyaloshinskii–Moriya Interaction (DMI) induced by spacereversal symmetry broken.Two dimensional (2D) non-centrosymmetric Janus could generate vertical builtin electric fields to break spatial inversion symmetry. Therefore, seeking 2D Janus with intrinsic magnetism is fundamental to develop the novel chiral magnetic storage technologies.In this work, we combined detailed machine learning techniques and first-principles calculations to discover the magnetism of the unexplored 2D janus. we first collected 1179 2D hexagonal ABC-type Janus based on the Materials Project database, and used elemental composition as feature descriptors to construct four machine learning models: Random Forest(RF), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGB), and Extra Trees(ET). These algorithms and models were constructed to predict lattice constants, formation energies, and magnetic moment, via hyperparameter optimization and ten-fold cross-validation. GBDT exhibits the highest accuracy and best prediction performance for magnetic moment classification. Subsequently, the collected data of 82,018 yet-undiscovered 2D Janus,were input into the trained models to generate 4,024 high magnetic moment 2D Janus with thermal stability. First-principles calculations were employed to validate random sample of 13 Janus with high magnetic moment. This study provides an effective machine learning framework for magnetic moment classification and high-throughput screening of 2D Janus, accelerating the exploration of magnetic properties in 2D Janus structures.
-
Keywords:
- machine learning /
- two-dimensional Janus materials /
- magnetic moment /
- first-principles calculations
-
[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 306666
[2] Zhang Z W, Lang Y F, Zhu H P, Li B, Zhao Y Q, Wei B, Zhou W X 2024 Phys. Rev. Appl. 21064012
[3] Liu B, Feng X X, Long M Q, Cai M Q, Yang J L 2022 Phys. Rev. Appl. 18054036
[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. 73137101
[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. 118173104
[6] Lang Y F, Zou D F, Xu Y, Jiang S L, Zhao Y Q, Ang Y S 2024 Appl. Phys. Lett. 124052903
[7] Liao C S, Ding Y F, Zhao Y Q, Cai M Q 2021 Appl. Phys. Lett. 119182903
[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. 8094414
[9] Liang J H, Wang W W, Du H F, Hallal A, Garcia K, Chshiev M, Fert A, Yang H X 2020 Phys. Rev. B 101184401
[10] Zhang S Q, Xu R Z, Luo N N, Zou X L 2021 Nanoscale 131398
[11] Dai C Y, He P, Luo L X, Zhan P X, Guan B, Zheng J 2023 Sci. China Mater. 66859
[12] Wang P, Zong Y X, Wen H Y, Xia J B, Wei Z M 2021 Acta Phys. Sin. 70026801
[13] Ren K, Wang K, Zhang G 2022 ACS Appl. Electron. Mater. 44507
[14] Peng Z L, Huang J X, Guo Z G 2021 Nanoscale 1318839
[15] 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 88813
[16] Vafaeezadeh M, Thiel W R 2022 Angew. Chem. Int. Edit. 61 e202206403
[17] Mukherjee T, Kar S, Ray S 2022 J. Mater. Res. 373418
[18] Li C Q, An Y K 2022 Phys. Rev. B 106115417
[19] Zhang L, Zhao Y, Liu Y Q, Gao G Y 2023 Nanoscale 1518910
[20] Xu L J, Wan W H, Peng Y R, Ge Y F, Liu Y 2024 Ann. Phys. 5362300388
[21] 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. 243460
[22] Liu H, Sun J T, Liu M, Meng S 2018 J. Phys. Chem. Lett. 96709
[23] Nelson J, Sanvito S 2019 Phys. Rev. Mater. 3104405
[24] Belot J F, Taufour V, Sanvito S, Hart G L 2023 Appl. Phys. Lett. 123042405
[25] Miyazato I, Tanaka Y, Takahashi K 2018 J. Phys.: Condens. Matter 3006LT01
[26] Lu S H, Zhou Q H, Guo Y L, Zhang Y H, Wu Y L, Wang J L 2020 Adv. Mater. 322002658
[27] Ma X Y, Lyu H Y, Hao K R, Zhao Y M, Qian X F, Yan Q B, Su G 2021 Sci. Bull. 66233
[28] 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. 152428
[29] Chaney G, Ibrahim A, Ersan F, Çakır D, Ataca C 2021 ACS Appl. Mater. Interfaces 1336388
[30] Yan X H, Zheng J M, Zhao X, Zhao P J, Guo P, Jiang Z Y 2024 Phys. Status Solidi Rapid Res. Lett. 182300468
[31] Jain A, Ong S P, Hautier G, Chen W, Richards W D, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G, Persson K A 2013 APL Mater. 1011002
[32] Chen P Y, Lam C H, Edmondson B, Posadas A B, Demkov A A, Ekerdt J G 2019 J. Vac. Sci. Technol. A 37050902
[33] Khushi M, Shaukat K, Alam T M, Hameed I A, Uddin S, Luo S, Yang X, Reyes M C 2021 IEEE Access 9109960
[34] 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. 15260
[35] Chen J, Song Y Y, Li S Z, Que Z X, Zhang W B 2023 Sci. China Technol. Sci. 1011002
[36] 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. 122825
[37] Ester M, Kriegel H P, Xu X 2023 Geogr. Anal. 55207
[38] Wu J, Chen X Y, Zhang H, Xiong L D, Lei H, Deng S H 2019 J. Electron. Sci. Technol. 1726
[39] Ma Q Y, Wan W H, Ge Y F, Li Y M, Liu Y 2022 J. Magn. Magn. Mater. 605172314
[40] Yin W J, Tan H J, Ding P J, Wen B, Li X B, Teobaldi G, Liu L M 2021 Mater. Adv. 27543
Metrics
- Abstract views: 56
- PDF Downloads: 5
- Cited By: 0
下载:
