Journal of the Chinese Ceramic Society, Volume. 51, Issue 2, 389(2023)

A Framework for Metal Surface Energy Prediction Based on Crystal Graph Convolutional Neural Network

ZHOU Linming1、*, ZHU Guangyu1, WU Yongjun1,2, HUANG Yuhui1, and HONG Zijian1,2
Author Affiliations
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  • 2[in Chinese]
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    References(34)

    [1] [1] LAI N C, CONG G, LIANG Z, et al. A highly active oxygen evolution catalyst for lithium-oxygen batteries enabled by high-surface-energy facets[J]. Joule, 2022, 2(8): 1511-1521.

    [2] [2] MA J, XING F, NAKAYA Y, et al. Nickel-based high-entropy intermetallic as a highly active and selective catalyst for acetylene semihydrogenation[J]. Angew Chem Int Ed, 2022, 61(27): e202200889.

    [3] [3] PIREZ C, LEE A F, JONES C, et al. Can surface energy measurements predict the impact of catalyst hydrophobicity upon fatty acid esterification over sulfonic acid functionalised periodic mesoporous organosilicas?[J]. Catal Today, 2014, 23(1): 167-173.

    [4] [4] VALSESIA A, DESMET C, OJEA-JIM?NEZ I, et al. Direct quantification of nanoparticle surface hydrophobicity[J]. Commun Chem, 2018, 1: 53.

    [5] [5] MICHIARDI A, APARICIO C, RATNER B D, et al. The influence of surface energy on competitive protein adsorption on oxidized NiTi surfaces[J]. Biomater, 2007, 28(4): 586-594.

    [6] [6] ZHOU K, CHEN J, WANG T, et al. Effect of surface energy on protein adsorption behaviours of treated CoCrMo alloy surfaces[J]. Appl Surf Sci, 2020, 520(1): 146354.

    [7] [7] HENSLEY A J R, COLLINGE G, WANG Y, et al. Coverage-dependent adsorption of hydrogen on Fe(100): Determining catalytically relevant surface structures via lattice gas models[J]. J Phys Chem C, 2020, 124(13): 7254-7266.

    [8] [8] STIRNER T, SCHOLZ D, SUN J, et al. Ab initio simulation of structure and surface energy of low-index surfaces of stoichiometric α-Fe2O3[J]. Surf Sci, 2018, 671: 11-16.

    [9] [9] WU M, ZHANG Z, XU X, et al. Seeded growth of large single-crystal copper foils with high-index facets[J]. Nature, 2020, 581: 406-410.

    [10] [10] LI S, FU J, MIAO G, et al. Toward planar and dendrite-free Zn electrodepositions by regulating Sn-crystal textured surface[J]. Adv Mater, 2021, 33: 2008424.

    [11] [11] GAO X, ZHOU Y, HAN D, et al. Thermodynamic understanding of Li-dendrite formation[J]. Joule, 2020, 4(9): 1864-1879.

    [12] [12] DU J, ZHANG A, GUO Z, et al. Atomistic underpinnings for growth direction and pattern formation of hcp magnesium alloy dendrite[J]. Acta Mater, 2018, 161: 35-46.

    [13] [13] TOYAO T, SUZUKI K, KIKUCHI S, et al. Toward effective utilization of methane: machine learning prediction of adsorption energies on metal alloys[J]. J Phys Chem C, 2018, 122(15): 8315-8326.

    [14] [14] WILLIAMS T, MCCULLOUGH K, LAUTERBACH J. Enabling catalyst discovery through machine learning and high-throughput experimentation[J]. Chem Mater, 2020, 32(1): 157-165.

    [15] [15] CHEN C, YE W, ZUO Y, et al. Graph networks as a universal machine learning framework for molecules and crystals[J]. Chem Mater, 2019, 31(9): 3564-3572.

    [16] [16] GILMER J, SCHOENHOLZ S S, RILEY F P, et al. Neural message passing for quantum chemistry[J]. Arxiv, 2017. Doi:10.48550/arXiv.1704.01212.

    [17] [17] GASTEIGER J, GRO? J, G?NNEMANN S. Directional message passing for molecular graphs[J]. Arxiv, 2020. Doi:10.48550/arXiv.2003.03123.

    [18] [18] CHENG J, ZHANG C, DONG L. A geometric-information-enhanced crystal graph network for predicting properties of materials[J]. Commun Mater, 2021, 2: 92.

    [19] [19] KEARNES S, MCCLOSKEY K, BERNDL M, et al. Molecular graph convolutions: Moving beyond fingerprints[J]. J Comput-Aided Mol Des, 2016, 30(8): 595-608.

    [20] [20] XIE T, GROSSMAN J. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties[J]. Phys Rev Lett, 2018, 120(14): 145301.

    [21] [21] AHMAD Z, XIE T, MAHESHWARI C, et al. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes[J]. ACS Cent Sci, 2018, 4(8): 996-1006.

    [22] [22] ZHOU L, YAO M A,WU Y, et al. Machine learning assisted prediction of cathode materials for Zn-ion batteries[J]. Adv Theory Simul, 2021, 4: 2100196.

    [23] [23] JAIN A, ONG S P, HAUTIER G. Commentary:The materials project: A materials genome approach to accelerating materials innovation[J]. APL Mater, 2013, 1: 011002.

    [24] [24] DE JONG M, CHEN W, ANGSTEN T, et al. Charting the complete elastic properties of inorganic crystalline compounds[J]. Sci Data, 2015, 2: 150009.

    [25] [25] MORTENSEN J J, HANSEN L B, JACOBSEN K W. Real-space grid implementation of the projector augmented wave method[J]. Phys Rev B, 2005, 71: 035109.

    [26] [26] ENKOVAARA J, ROSTGAARD C, MORTENSEN J J, et al. Electronic structure calculations with GPAW: A real-space implementation of the projector augmented-wave method[J]. J Phys: Condens Matter, 2010, 22: 253202.

    [27] [27] BL?CHL P E. Projector augmented-wave method[J]. Phys Rev B, 1994, 50: 17953.

    [28] [28] BL?CHL P E, F?RST C J, SCHIMPL J. Projector augmented wave method: Ab-initio molecular dynamics with full wave functions[J]. Bull Mater Sci, 2003, 26: 33.

    [29] [29] LARSEN A H, MORTENSEN J J, BLOMQVIST J, et al. The atomic simulation environment-a python library for working with atoms[J]. J Phys: Condens Matter, 2017, 29: 273002.

    [30] [30] BAHN S R, JACOBSEN K W. An object-oriented scripting interface to a legacy electronic structure code[J]. Comput Sci Eng, 2002, 4: 56-66.

    [31] [31] PERDEW J P, ERNZERHOF M. Rationale for mixing exact exchange with density functional approximations[J]. J Chem Phys, 1996, 105: 9982-9985.

    [32] [32] KANDASAMY K, VYSYARAJU K R, NEISWANGER W, et al. tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly[J]. J Mach Learn Res, 2020, 21(81): 1-27.

    [33] [33] YU S, CHAI H, XIONG Y, et al. Studying complex evolution of hyperelastic materials under external field stimuli using artificial neural networks with spatiotemporal features in a small-scale dataset[J]. Adv Mater, 2022, 34: 2200908.

    [34] [34] ZHANG X, XU J, YANG J, et al. Understanding the learning mechanism of convolutional neural networks in spectral analysis[J]. Anal Chim Acta, 2020, 1119: 41-51.

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    ZHOU Linming, ZHU Guangyu, WU Yongjun, HUANG Yuhui, HONG Zijian. A Framework for Metal Surface Energy Prediction Based on Crystal Graph Convolutional Neural Network[J]. Journal of the Chinese Ceramic Society, 2023, 51(2): 389

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    Paper Information

    Special Issue:

    Received: Sep. 28, 2022

    Accepted: --

    Published Online: Mar. 11, 2023

    The Author Email: Linming ZHOU (linming.zhou@zju.edu.cn; gy_zhu@zju.edu.cn)

    DOI:10.14062/j.issn.0454-5648.20220802

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