| CONDENSED MATTER: ELECTRONIC STRUCTURE, ELECTRICAL, MAGNETIC, AND OPTICAL PROPERTIES |
|
|
|
|
Self-Supervised Graph Neural Networks for Accurate Prediction of Néel Temperature |
| Jian-Gang Kong1, Qing-Xu Li1,2, Jian Li1,2,3, Yu Liu4, and Jia-Ji Zhu1,2,3* |
1School of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
3Southwest Center for Theoretical Physics, Chongqing University, Chongqing 401331, China
4Inspur Electronic Information Industry Co., Ltd, Beijing 100085, China
|
|
| Cite this article: |
|
Jian-Gang Kong, Qing-Xu Li, Jian Li et al 2022 Chin. Phys. Lett. 39 067503 |
|
|
|
|
Abstract Antiferromagnetic materials are exciting quantum materials with rich physics and great potential for applications. On the other hand, an accurate and efficient theoretical method is highly demanded for determining critical transition temperatures, Néel temperatures, of antiferromagnetic materials. The powerful graph neural networks (GNNs) that succeed in predicting material properties lose their advantage in predicting magnetic properties due to the small dataset of magnetic materials, while conventional machine learning models heavily depend on the quality of material descriptors. We propose a new strategy to extract high-level material representations by utilizing self-supervised training of GNNs on large-scale unlabeled datasets. According to the dimensional reduction analysis, we find that the learned knowledge about elements and magnetism transfers to the generated atomic vector representations. Compared with popular manually constructed descriptors and crystal graph convolutional neural networks, self-supervised material representations can help us to obtain a more accurate and efficient model for Néel temperatures, and the trained model can successfully predict high Néel temperature antiferromagnetic materials. Our self-supervised GNN may serve as a universal pre-training framework for various material properties.
|
|
|
Received: 09 April 2022
Editors' Suggestion
Published: 29 May 2022
|
|
| PACS: |
75.50.Ee
|
(Antiferromagnetics)
|
| |
07.05.Mh
|
(Neural networks, fuzzy logic, artificial intelligence)
|
| |
77.80.B-
|
(Phase transitions and Curie point)
|
|
|
|
|
|
| [1] | Lee P A, Nagaosa N, and Wen X G 2006 Rev. Mod. Phys. 78 17 |
| [2] | Zhou Y, Kanoda K, and Ng T K 2017 Rev. Mod. Phys. 89 025003 |
| [3] | Qiao Z, Ren W, Chen H, Bellaiche L, Zhang Z, MacDonald A H, and Niu Q 2014 Phys. Rev. Lett. 112 116404 |
| [4] | Liu C, Wang Y, Li H, Wu Y, Li Y, Li J, He K, Xu Y, Zhang J, and Wang Y 2020 Nat. Mater. 19 522 |
| [5] | Park B G, Wunderlich J, Martı́ X, Holỳ V, Kurosaki Y, Yamada M, Yamamoto H, Nishide A, Hayakawa J, Takahashi H, Shick A B, and Jungwirth T 2011 Nat. Mater. 10 347 |
| [6] | Qiu Z, Hou D, Barker J, Yamamoto K, Gomonay O, and Saitoh E 2018 Nat. Mater. 17 577 |
| [7] | Wadley P, Howells B, Železnỳ J, Andrews C, Hills V, Campion R P, Novák V, Olejnı́k K, Maccherozzi F, SS D, Martin S Y, Wanger T, Wunderlich J, Freimuth F, Mokrosov Y, Kuneš J, Chauhan J S, Grzybowski M J, Rushforth A W, Edmonds K W, Gallagher B L, and Jungwirth T 2016 Science 351 587 |
| [8] | Jungwirth T, Marti X, Wadley P, and Wunderlich J 2016 Nat. Nanotechnol. 11 231 |
| [9] | Baltz V, Manchon A, Tsoi M, Moriyama T, Ono T, and Tserkovnyak Y 2018 Rev. Mod. Phys. 90 015005 |
| [10] | Li X, Hongyu Y, Feng L, Feng J S, Whangbo M H, and Xiang H 2021 Molecules 26 803 |
| [11] | Loh E Y, Gubernatis J E, Scalettar R T, White S R, Scalapino D J, and Sugar R L 1990 Phys. Rev. B 41 9301 |
| [12] | Didier P, Matthieu M, and Fabien A 2021 SciPost Phys. 10 19 |
| [13] | Li W, Ran S J, Gong S S, Zhao Y, Xi B, Ye F, and Su G 2011 Phys. Rev. Lett. 106 127202 |
| [14] | Czarnik P, Cincio L, and Dziarmaga J 2012 Phys. Rev. B 86 245101 |
| [15] | Rao W J 2020 Chin. Phys. Lett. 37 080501 |
| [16] | Cheng Z and Yu Z 2021 Chin. Phys. Lett. 38 070302 |
| [17] | Zhang R, Wei B, Zhang D, Zhu J J, and Chang K 2019 Phys. Rev. B 99 094427 |
| [18] | Lu H, Li C, Chen B B, Li W, Qi Y, and Meng Z Y 2022 Chin. Phys. Lett. 39 050701 |
| [19] | Ouyang Y, Zhang Z, Yu C, He J, Yan G, and Chen J 2020 Chin. Phys. Lett. 37 126301 |
| [20] | Schmidt J, Marques M R G, Botti S, and Marques M A L M 2019 npj Comput. Mater. 5 83 |
| [21] | Noh J, Gu G H, Kim S, and Jung Y 2020 Chem. Sci. 11 4871 |
| [22] | Xie T and Grossman J C 2018 Phys. Rev. Lett. 120 145301 |
| [23] | Chen C, Ye W, Zuo Y, Zheng C, and Ong S P 2019 Chem. Mater. 31 3564 |
| [24] | Schütt K T, Sauceda H E, Kindermans P J, Tkatchenko A, and Müller K R 2018 J. Chem. Phys. 148 241722 |
| [25] | Karamad M, Magar R, Shi Y, Siahrostami S, Gates I D, and Farimani A B 2020 Phys. Rev. Mater. 4 093801 |
| [26] | Park C W and Wolverton C 2020 Phys. Rev. Mater. 4 063801 |
| [27] | Nelson J and Sanvito S 2019 Phys. Rev. Mater. 3 104405 |
| [28] | Long T, M F N, Zhang Y, Gutfleisch O, and Zhang H 2021 Mater. Res. Lett. 9 169 |
| [29] | Nguyen D N, Pham T L, Nguyen V C, Nguyen A T, Kino H, Miyake A, and Dam H C 2019 J. Phys.: Conf. Ser. 1290 012009 |
| [30] | Lu K, Chang D, Lu T, Ji X, Li M, and Lu W 2021 J. Supercond. Novel Magn. 34 1961 |
| [31] | Court C and Cole J 2020 npj Comput. Mater. 6 18 |
| [32] | Dunn A, Wang Q, Ganose A, Dopp D, and Jain A 2020 npj Comput. Mater. 6 138 |
| [33] | Ghiringhelli L M, Vybiral J, Levchenko S V, Draxl C, and Scheffler M 2015 Phys. Rev. Lett. 114 105503 |
| [34] | Devlin J, Chang M W, Lee K, and Toutanova K 2018 arXiv:1810.04805 [cs.CL] |
| [35] | He K, Fan H, Wu Y, Xie S, and Girshick R 2019 arXiv:1911.05722 [cs.CV] |
| [36] | Hu W, Liu B, Gomes J, Zitnik M, Liang P, Pande V, and Leskovec J 2019 arXiv:1905.12265 [cs.LG] |
| [37] | Gilmer J, Schoenholz S S, Riley P F, Vinyals O, and Dahl G E 2017 arXiv:1704.01212 [cs.LG] |
| [38] | Jain A, Ong S P, Hautier G, Chen W, Richards W D, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G, and Persson K A 2013 APL Mater. 1 011002 |
| [39] | Li Q, Han Z, and Wu X M 2018 arXiv:1801.07606 [cs.LG] |
| [40] | van der Maaten L and Hinton G 2008 J. Mach. Learn. Res. 9 2579 |
| [41] | Gallego S V, Perez-Mato J M, Elcoro L, Tasci E S, Hanson R M, Momma K, Aroyo M I, and Madariaga G 2016 J. Appl. Crystallogr. 49 1750 |
| [42] | Gallego S V, Perez-Mato J M, Elcoro L, Tasci E S, Hanson R M, Momma K, Aroyo M I, and Madariaga G 2016 J. Appl. Crystallogr. 49 1941 |
| [43] | Faber F, Lindmaa A, von Lilienfeld O A, and Armiento R 2015 Int. J. Quantum Chem. 115 1094 |
| [44] | Pham T L, Kino H, Terakura K, Miyake T T I, Tsuda K, and Dam H C 2017 Sci. Technol. Adv. Mater. 18 756 |
| [45] | Jenkins S, Chantrell R W, and Evans R F L 2021 Phys. Rev. B 103 014424 |
| [46] | Kuo C Y, Drees Y, Fernández-Díaz M T, Zhao L, Vasylechko L, Sheptyakov D, Bell A M T, Pi T W, Lin H J, Wu M K, Pellegrin E, Valvidares S M, Li Z W, Adler P, Todorova A, Küchler R, Steppke A, Tjeng L H, Hu Z, and Komarek A C 2014 Phys. Rev. Lett. 113 217203 |
| [47] | Hiley C I, Scanlon D O, Sokol A A, Woodley S M, Ganose A M, Sangiao S, De Teresa J M, Manuel P, Khalyavin D D, Walker M, Lees M R, and Walton R I 2015 Phys. Rev. B 92 104413 |
| [48] | Tomeno I, Fuke H N, Iwasaki H, Sahashi M, and Tsunoda Y 1999 J. Appl. Phys. 86 3853 |
| [49] | Collomb A, Wolfers P, and Obradors X 1986 J. Magn. Magn. Mater. 62 57 |
| [50] | Bertaut E F, Chappert J, Mareschal J, Rebouillat J P, and Sivardière J 1967 Solid State Commun. 5 293 |
| [51] | Bronstein M M, Bruna J, Cohen T, and Veličković P 2021 arXiv:2104.13478 [cs.LG] |
| [52] | Xie T, Bapst V, Gaunt A L, Obika A, Back T, Demis H, Kohli P, and Kirkpatrick J 2021 arXiv:2103.13795 [cond-mat.mtrl-sci] |
| [53] | Chen C, Zuo Y, Ye W, Li X G, and Ong S 2021 Nat. Comput. Sci. 1 46 |
| [54] | Lee J and Asahi R 2021 Comput. Mater. Sci. 190 110314 |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
