Reconstruction and stability of Fe3O4(001) surface: An investigation based on particle swarm optimization and machine learning

doi:10.1088/1674-1056/acb9e4

中国物理B ›› 2023, Vol. 32 ›› Issue (5): 56802-056802.doi: 10.1088/1674-1056/acb9e4

所属专题： SPECIAL TOPIC — Smart design of materials and design of smart materials

Reconstruction and stability of Fe₃O₄(001) surface: An investigation based on particle swarm optimization and machine learning

Hongsheng Liu(柳洪盛), Yuanyuan Zhao(赵圆圆), Shi Qiu(邱实), Jijun Zhao(赵纪军), and Junfeng Gao(高峻峰)^†

Key Laboratory of Materials Modification by Laser, Ion and Electron Beams(Dalian University of Technology), Ministry of Education, Dalian 116024, China

收稿日期:2023-01-15 修回日期:2023-02-02 接受日期:2023-02-08 出版日期:2023-04-21 发布日期:2023-04-26
通讯作者: Junfeng Gao E-mail:gaojf@dlut.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 12004064, 12074053, and 91961204), the Fundamental Research Funds for the Central Universities (Grant No. DUT22LK11) and XingLiaoYingCai Project of Liaoning Province, China (Grant No. XLYC1907163). The calculations were performed on Tianjin Supercomputing Platform, Shanghai Supercomputing Platform.

Reconstruction and stability of Fe₃O₄(001) surface: An investigation based on particle swarm optimization and machine learning

Hongsheng Liu(柳洪盛), Yuanyuan Zhao(赵圆圆), Shi Qiu(邱实), Jijun Zhao(赵纪军), and Junfeng Gao(高峻峰)^†

Key Laboratory of Materials Modification by Laser, Ion and Electron Beams(Dalian University of Technology), Ministry of Education, Dalian 116024, China

Received:2023-01-15 Revised:2023-02-02 Accepted:2023-02-08 Online:2023-04-21 Published:2023-04-26
Contact: Junfeng Gao E-mail:gaojf@dlut.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 12004064, 12074053, and 91961204), the Fundamental Research Funds for the Central Universities (Grant No. DUT22LK11) and XingLiaoYingCai Project of Liaoning Province, China (Grant No. XLYC1907163). The calculations were performed on Tianjin Supercomputing Platform, Shanghai Supercomputing Platform.

1. 附件.pdf(2411KB)

摘要/Abstract

摘要： Magnetite nanoparticles show promising applications in drug delivery, catalysis, and spintronics. The surface of magnetite plays an important role in these applications. Therefore, it is critical to understand the surface structure of Fe₃O₄ at atomic scale. Here, using a combination of first-principles calculations, particle swarm optimization (PSO) method and machine learning, we investigate the possible reconstruction and stability of Fe₃O₄(001) surface. The results show that besides the subsurface cation vacancy (SCV) reconstruction, an A layer with Fe vacancy (A-layer-V_Fe) reconstruction of the (001) surface also shows very low surface energy especially at oxygen poor condition. Molecular dynamics simulation based on the iron-oxygen interaction potential function fitted by machine learning further confirms the thermodynamic stability of the A-layer-V_Fe reconstruction. Our results are also instructive for the study of surface reconstruction of other metal oxides.

关键词: surface reconstruction, magnetite surface, particle swarm optimization, machine learning

Abstract: Magnetite nanoparticles show promising applications in drug delivery, catalysis, and spintronics. The surface of magnetite plays an important role in these applications. Therefore, it is critical to understand the surface structure of Fe₃O₄ at atomic scale. Here, using a combination of first-principles calculations, particle swarm optimization (PSO) method and machine learning, we investigate the possible reconstruction and stability of Fe₃O₄(001) surface. The results show that besides the subsurface cation vacancy (SCV) reconstruction, an A layer with Fe vacancy (A-layer-V_Fe) reconstruction of the (001) surface also shows very low surface energy especially at oxygen poor condition. Molecular dynamics simulation based on the iron-oxygen interaction potential function fitted by machine learning further confirms the thermodynamic stability of the A-layer-V_Fe reconstruction. Our results are also instructive for the study of surface reconstruction of other metal oxides.

Key words: surface reconstruction, magnetite surface, particle swarm optimization, machine learning

中图分类号: (Solid surfaces and solid-solid interfaces: structure and energetics)

68.35.-p

68.35.B- (Structure of clean surfaces (and surface reconstruction))

Hongsheng Liu(柳洪盛), Yuanyuan Zhao(赵圆圆), Shi Qiu(邱实), Jijun Zhao(赵纪军), and Junfeng Gao(高峻峰). Reconstruction and stability of Fe₃O₄(001) surface: An investigation based on particle swarm optimization and machine learning[J]. 中国物理B, 2023, 32(5): 56802-056802.

参考文献

[1] Mitchell M J, Billingsley M M, Haley R M, Wechsler M E, Peppas N A and Langer R 2021 Nat. Rev. Drug Discov. 20 101
[2] Colombo M, Carregal-Romero S, Casula M F, Gutiérrez L, Morales M P, Böhm I B, Heverhagen J T, Prosperi D and Parak W J 2012 Chem. Soc. Rev. 41 4306
[3] Chee H L, Gan C R R, Ng M, Low L, Fernig D G, Bhakoo K K and Paramelle D 2018 ACS Nano 12 6480
[4] Sun S N, We C, Zhu Z Z, Hou Y L, Venkatraman Subbu S and Xu Z C 2014 Chin. Phys. B 23 037503
[5] Li W Y, Li W T, Li B Q, Dong L J, Meng T H, Huo G, Liang G Y and Lu X G 2021 Chin. Phys. B 30 104402
[6] Eerenstein W, Palstra T T M, Saxena S S and Hibma T 2002 Phys. Rev. Lett. 88 247204
[7] Huang Z, Liu W, Liang J, Guo Q, Zhai Y and Xu Y 2022 Chin. Phys. B 31 068505
[8] Zhu M and Wachs I E 2016 ACS Catal. 6 722
[9] Sharma R K, Dutta S, Sharma S, Zboril R, Varma R S and Gawande M B 2016 Green Chem. 18 3184
[10] Verwey E J W 1939 Nature 144 327
[11] Liu H and Di Valentin C 2017 J. Phys. Chem. C 121 25736
[12] Parkinson G S 2016 Surf. Sci. Rep. 71 272
[13] Gaines J M, Bloemen P J H, Kohlhepp J T, Bulle-Lieuwma C W T, Wolf R M, Reinders A, Jungblut R M, van der Heijden P A A, van Eemeren J T W M, de Stegge J A and de Jonge W J M 1997 Surf. Sci. 373 85
[14] Tarrach G, Bürgler D, Schaub T, Wiesendanger R and Güntherodt H J 1993 Surf. Sci. 285 1
[15] Chambers S A, Thevuthasan S and Joyce S A 2000 Surf. Sci. 450 L273
[16] Mijiritskii A V and Boerma D O 2001 Surf. Sci. 486 73
[17] Stanka B, Hebenstreit W, Diebold U and Chambers S A 2000 Surf. Sci. 448 49
[18] Voogt F C, Fujii T, Smulders P J M, Niesen L, James M A and Hibma T 1999 Phys. Rev. B 60 11193
[19] Pentcheva R, Wendler F, Meyerheim H L, Moritz W, Jedrecy N and Scheffler M 2005 Phys. Rev. Lett. 94 126101
[20] Bliem R, McDermott E, Ferstl P, Setvin M, Gamba O, Pavelec J, Schneider M A, Schmid M, Diebold U, Blaha P, Hammer L and Parkinson G S 2014 Science 346 1215
[21] Lodziana Z 2007 Phys. Rev. Lett. 99 206402
[22] Liu H and Di Valentin C 2018 Nanoscale 10 11021
[23] Novotny Z, Argentero G, Wang Z, Schmid M, Diebold U and Parkinson G S 2012 Phys. Rev. Lett. 108 216103
[24] Wang P, Xing J, Jiang X and Zhao J 2022 ACS Appl. Mater. Interfaces 14 33726
[25] Mao X, Wang L, Xu Y, Wang P, Li Y and Zhao J 2021 npj Comput. Mater. 7 46
[26] Peng F, Sun Y, Pickard C J, Needs R J, Wu Q and Ma Y 2017 Phys. Rev. Lett. 119 107001
[27] Zhong X, Wang H, Zhang J, Liu H, Zhang S, Song H-F, Yang G, Zhang L and Ma Y 2016 Phys. Rev. Lett. 116 057002
[28] Shao G F, Zhu M, Shangguan Y L, Li W R, Zhang C, Wang W W and Li L 2017 Chin. Phys. B 26 063601
[29] Sun Y and Hu W 2021 SmartMat 3 474
[30] Sun Z, Yin H, Liu K, Cheng S, Li G K, Kawi S, Zhao H, Jia G and Yin Z 2022 SmartMat 3 68
[31] Kresse G and Furthmüller J 1996 Phys. Rev. B 54 11169
[32] Kresse G and Joubert D 1999 Phys. Rev. B 59 1758
[33] Perdew J P, Burke K and Ernzerhof M 1996 Phys. Rev. Lett. 77 3865
[34] Behler J 2011 J. Chem. Phys. 134 074106
[35] Artrith N, Urban A and Ceder G 2017 Phys. Rev. B 96 014112
[36] Reuter K and Scheffler M 2001 Phys. Rev. B 65 035406
[37] Henkelman G, Arnaldsson A and Jónsson H 2006 Comput. Mater. Sci. 36 354
[38] Sanville E, Kenny S D, Smith R and Henkelman G 2007 J. Comput. Chem. 28 899
[39] Jordan K, Cazacu A, Manai G, Ceballos S F, Murphy S and Shvets I V 2006 Phys. Rev. B 74 085416
[40] Liu H and Di Valentin C 2019 Phys. Rev. Lett. 123 186101
[41] Sun S and Zeng H 2002 J. Am. Chem. Soc. 124 8204
[42] Hou Y, Yu J and Gao S 2003 J. Mater. Chem. 13 1983
[43] Sun S, Zeng H, Robinson D B, Raoux S, Rice P M, Wang S X and Li G 2004 J. Am. Chem. Soc. 126 273
[44] Park J, An K, Hwang Y, Park J G, Noh H J, Kim J Y, Park J H, Hwang N M and Hyeon T 2004 Nat. Mater. 3 891
[45] Tian Y, Yu B, Li X and Li K 2011 J. Mater. Chem. 21 2476

Reconstruction and stability of Fe₃O₄(001) surface: An investigation based on particle swarm optimization and machine learning

Reconstruction and stability of Fe₃O₄(001) surface: An investigation based on particle swarm optimization and machine learning

RichHTML

PDF (PC)

赞

补充材料

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	Zhanjun Qiu(邱占均), Yanxiao Hu(胡晏箫), Ding Li(李顶), Tao Hu(胡涛), Hong Xiao(肖红),Chunbao Feng(冯春宝), and Dengfeng Li(李登峰). Thermal transport properties of two-dimensional boron dichalcogenides from a first-principles and machine learning approach[J]. 中国物理B, 2023, 32(5): 54402-054402.
[2]	Yilin Zhang(张轶霖), Huimin Mu(穆慧敏), Yuxin Cai(蔡雨欣), Xiaoyu Wang(王啸宇), Kun Zhou(周琨), Fuyu Tian(田伏钰), Yuhao Fu(付钰豪), and Lijun Zhang(张立军). Evaluating thermal expansion in fluorides and oxides: Machine learning predictions with connectivity descriptors[J]. 中国物理B, 2023, 32(5): 56302-056302.
[3]	Xiaoyi Ma(马宵怡), Yufeng Luo(罗宇峰), Mengke Li(李梦可), Wenyan Jiao(焦文艳), Hongmei Yuan(袁红梅), Huijun Liu(刘惠军), and Ying Fang(方颖). Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles[J]. 中国物理B, 2023, 32(5): 57306-057306.
[4]	Ao Chen(陈奥), Hua Tong(童话), Cheng-Wei Wu(吴成伟), Guofeng Xie(谢国锋), Zhong-Xiang Xie(谢忠祥), Chang-Qing Xiang(向长青), and Wu-Xing Zhou(周五星). Stress effect on lattice thermal conductivity of anode material NiNb₂O₆ for lithium-ion batteries[J]. 中国物理B, 2023, 32(5): 58201-058201.
[5]	Yi-Han Wang(王奕涵) and Hai-Feng Zhang(章海锋). Angular insensitive nonreciprocal ultrawide band absorption in plasma-embedded photonic crystals designed with improved particle swarm optimization algorithm[J]. 中国物理B, 2023, 32(4): 44207-044207.
[6]	Jinlong Hu(胡锦龙), Yuting Zuo(左钰婷), Yuzhou Hao(郝昱州), Guoyu Shu(舒国钰), Yang Wang(王洋), Minxuan Feng(冯敏轩), Xuejie Li(李雪洁), Xiaoying Wang(王晓莹), Jun Sun(孙军), Xiangdong Ding(丁向东), Zhibin Gao(高志斌), Guimei Zhu(朱桂妹), and Baowen Li(李保文). Prediction of lattice thermal conductivity with two-stage interpretable machine learning[J]. 中国物理B, 2023, 32(4): 46301-046301.
[7]	Xue-Yi Guo(郭学仪), Shang-Shu Li(李尚书), Xiao Xiao(效骁), Zhong-Cheng Xiang(相忠诚), Zi-Yong Ge(葛自勇), He-Kang Li(李贺康), Peng-Tao Song(宋鹏涛), Yi Peng(彭益), Zhan Wang(王战), Kai Xu(许凯), Pan Zhang(张潘), Lei Wang(王磊), Dong-Ning Zheng(郑东宁), and Heng Fan(范桁). Variational quantum simulation of thermal statistical states on a superconducting quantum processer[J]. 中国物理B, 2023, 32(1): 10307-010307.
[8]	Jing Yue(岳靖), Jian Li(李剑), Wen Zhang(张文), and Zhangxin Chen(陈掌星). The coupled deep neural networks for coupling of the Stokes and Darcy-Forchheimer problems[J]. 中国物理B, 2023, 32(1): 10201-010201.
[9]	Yong Huang(黄勇) and Yang Li(李扬). Data-driven modeling of a four-dimensional stochastic projectile system[J]. 中国物理B, 2022, 31(7): 70501-070501.
[10]	Tonghe Ying(应通和), Jianbao Zhu(朱健保), and Wenguang Zhu(朱文光). Machine learning potential aided structure search for low-lying candidates of Au clusters[J]. 中国物理B, 2022, 31(7): 78402-078402.
[11]	Shi-Jie Pan(潘世杰), Lin-Chun Wan(万林春), Hai-Ling Liu(刘海玲), Yu-Sen Wu(吴宇森), Su-Juan Qin(秦素娟), Qiao-Yan Wen(温巧燕), and Fei Gao(高飞). Quantum algorithm for neighborhood preserving embedding[J]. 中国物理B, 2022, 31(6): 60304-060304.
[12]	Ruoting Zhao(赵若廷), Bangyu Xing(邢邦昱), Huimin Mu(穆慧敏), Yuhao Fu(付钰豪), and Lijun Zhang(张立军). Evaluation of performance of machine learning methods in mining structure—property data of halide perovskite materials[J]. 中国物理B, 2022, 31(5): 56302-056302.
[13]	Yan-Yan Hou(侯艳艳), Jian Li(李剑), Xiu-Bo Chen(陈秀波), and Yuan Tian(田源). Quantum partial least squares regression algorithm for multiple correlation problem[J]. 中国物理B, 2022, 31(3): 30304-030304.
[14]	Yinlu Gao(高寅露), Kai Cheng(程开), Xue Jiang(蒋雪), and Jijun Zhao(赵纪军). Interface engineering of transition metal dichalcogenide/GaN heterostructures: Modified broadband for photoelectronic performance[J]. 中国物理B, 2022, 31(11): 117304-117304.
[15]	Jintao Yang(杨锦涛), Junpeng Cao(曹俊鹏), and Wen-Li Yang(杨文力). Dynamical learning of non-Markovian quantum dynamics[J]. 中国物理B, 2022, 31(1): 10314-010314.