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A Framework for Metal Surface Energy Prediction Based on Crystal Graph Convolutional Neural Network
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Linming ZHOU1, Guangyu ZHU1, Yongjun WU1, 2, Yuhui HUANG1, Zijian HONG1, 2
Journal of the Chinese Ceramic Society | 2023, 51(2) : 389 - 396
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Journal of the Chinese Ceramic Society | 2023, 51(2): 389-396
• Research Articles •
A Framework for Metal Surface Energy Prediction Based on Crystal Graph Convolutional Neural Network
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Linming ZHOU1, Guangyu ZHU1, Yongjun WU1, 2, Yuhui HUANG1, Zijian HONG1, 2
Affiliations
  • 1.School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
  • 2.Cyrus Tang Center for Sensor Materials and Applications, State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou 310027, China

    • ZHOU Linming (1998-), male, Doctoral candidate. E-mail:

    ZHU Guangyu (2001-), male, Undergraduate.

About Author:

ZHOU Linming (1998-), male, Doctoral candidate. E-mail:

ZHU Guangyu (2001-), male, Undergraduate.

Published: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220802
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Surface energy is one of the most important physical and chemical properties for crystals, which has a significant impact on surface catalysis, surface adsorption, epitaxial growth, nucleation, and dendrite growth. Rapid calculation and prediction of crystal surface energies can favor accelerating the design and optimization of catalysis materials, battery materials, and alloys. In this paper, a data-driven machine learning algorithm was proposed with a crystal graph convolutional neural network framework for the prediction of metal surface energy from the crystal structure. Using a physics-based surface representation that couples the surface dimensions to the atomic and bonding features of the crystal, we obtained an MAE value of less than 0.002 eV/Å2, which surpasses other math-based surface models. Compared with the first-principles calculation, the computation time is reduced by approxiamtely 5 orders of magnitude. In addition, we discussed the main challenges and solutions towards the surface energy prediction of more complicated systems such as Silicates. It is expected that this work could be a paradigm for the surface energy prediction with machine learning.

machine learning  /  material surface  /  surface energy  /  graph neural network
周麟铭, 朱广宇, 吴勇军, 黄玉辉, 洪子健. 基于晶体图卷积神经网络的金属表面能预测框架. 硅酸盐学报, 2023 , 51 (2) : 389 -396 . DOI: 10.14062/j.issn.0454-5648.20220802
Linming ZHOU, Guangyu ZHU, Yongjun WU, Yuhui HUANG, Zijian HONG. 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 -396 . DOI: 10.14062/j.issn.0454-5648.20220802
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Year 2023 volume 51 Issue 2
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Article Info
doi: 10.14062/j.issn.0454-5648.20220802
  • Receive Date：2022-09-28
  • Online Date：2026-10-20
  • Published：2023-02-20
Article Data
Affiliations
History
  • Received：2022-09-28
  • Revised：2022-11-14
Funding
Affiliations
    1.School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
    2.Cyrus Tang Center for Sensor Materials and Applications, State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou 310027, China

Corresponding:

WU Yongjun (1974-), male, Ph.D., Professor; E-mail:
HONG Zijian (1989-), male, Ph.D., Professor.
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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