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Article(id=1319222571720102608, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220802, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1664294400000, receivedDateStr=2022-09-28, revisedDate=1668355200000, revisedDateStr=2022-11-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456771068, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456771068, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456771068, creator=13701087609, updateTime=1792456771068, updator=13701087609, issue=Issue{id=1319222524555153701, tenantId=1146029695717560320, journalId=1263187385517883426, year='2023', volume='51', issue='2', pageStart='283', pageEnd='560', issueExtLink='null', onlineDate='null', pubDate='1676822400000', pubDateStr='2023-02-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1792456759823, creator='13701087609', updateTime=1792456885477, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319223051636560419, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319223051636560420, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=389, endPage=396, ext={EN=ArticleExt(id=1319222571921429201, articleId=1319222571720102608, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=A Framework for Metal Surface Energy Prediction Based on Crystal Graph Convolutional Neural Network, columnId=1321891532131652078, journalTitle=Journal of the Chinese Ceramic Society, columnName=Application of Machine Learning in Material Research & Development—Research Articles, runingTitle=null, highlight=null, articleAbstract=

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.

, authors=Linming ZHOU1, Guangyu ZHU1, Yongjun WU1, 2, Yuhui HUANG1, Zijian HONG1, 2, authorsList=Linming ZHOU, Guangyu ZHU, Yongjun WU, Yuhui HUANG, Zijian HONG, authorCompany=null, correspAuthors=Yongjun WU, Zijian HONG, authorNote=

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

ZHU Guangyu (2001-), male, Undergraduate.

, correspAuthorsNote=
WU Yongjun (1974-), male, Ph.D., Professor; E-mail:
HONG Zijian (1989-), male, Ph.D., Professor.
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1319222572160504530, articleId=1319222571720102608, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=基于晶体图卷积神经网络的金属表面能预测框架, columnId=1321891532337172975, journalTitle=硅酸盐学报, columnName=机器学习在材料研发中的应用专题—研究论文, runingTitle=null, highlight=null, articleAbstract=

表面能是晶体最重要的物理化学性质之一，对于表面催化、表面吸附、外延生长、结晶形核、枝晶生长等具有十分重要的影响。快速计算和预测晶体表面能有助于加速催化、电池、合金等材料的设计和优化。本工作采用基于数据驱动的机器学习算法，构建了晶体图卷积神经网络框架并应用于金属表面能预测，实现了从晶体结构到表面能的快速准确预测。使用基于物理理论的将表面尺寸与晶体的原子以及成键特征相耦合的表面表示方法时，模型表现最佳，平均绝对误差值小于0.002 eV/Å2。与第一性原理计算相比，该框架计算时间缩短了约5个数量级。最后，还进一步探讨了其在硅酸盐等复杂体系的普适性和未来的拓展方向，以期为机器学习预测表面能的提供参考。

, authors=周麟铭1, 朱广宇1, 吴勇军1, 2, 黄玉辉1, 洪子健1, 2, authorsList=周麟铭, 朱广宇, 吴勇军, 黄玉辉, 洪子健, authorCompany=null, correspAuthors=吴勇军, 洪子健, authorNote=

周麟铭（1998—），男，博士研究生；

朱广宇（2001—），男，本科生。

, correspAuthorsNote=
吴勇军（1974—），男，博士，教授；
洪子健（1989—），男，博士，研究员。
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周麟铭（1998—），男，博士研究生；

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周麟铭（1998—），男，博士研究生；

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ZHU Guangyu (2001-), male, Undergraduate.

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朱广宇（2001—），男，本科生。

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朱广宇（2001—），男，本科生。

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基于晶体图卷积神经网络的金属表面能预测框架
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周麟铭 1 , 朱广宇 1 , 吴勇军 1, 2 , 黄玉辉 1 , 洪子健 1, 2
硅酸盐学报 | 机器学习在材料研发中的应用专题—研究论文 2023,51(2): 389-396
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硅酸盐学报 |机器学习在材料研发中的应用专题—研究论文 2023 , 51 (2) : 389 -396
基于晶体图卷积神经网络的金属表面能预测框架
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朱广宇（2001—），男，本科生。

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周麟铭1 , 朱广宇1 , 吴勇军1, 2 , 黄玉辉1, 洪子健1, 2
作者信息
  • 1．浙江大学材料科学与工程学院，杭州 310027
  • 2．唐仲英传感材料及应用研究中心，浙江大学硅材料国家重点实验室，杭州 310027
通讯作者:
吴勇军（1974—），男，博士，教授；
洪子健（1989—），男，博士，研究员。
作者简介:

周麟铭（1998—），男，博士研究生；

朱广宇（2001—），男，本科生。

A Framework for Metal Surface Energy Prediction Based on Crystal Graph Convolutional Neural Network
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
    • Corresponding Author:
    WU Yongjun (1974-), male, Ph.D., Professor; E-mail:
    HONG Zijian (1989-), male, Ph.D., Professor.

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

    ZHU Guangyu (2001-), male, Undergraduate.

出版时间: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220802
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表面能是晶体最重要的物理化学性质之一，对于表面催化、表面吸附、外延生长、结晶形核、枝晶生长等具有十分重要的影响。快速计算和预测晶体表面能有助于加速催化、电池、合金等材料的设计和优化。本工作采用基于数据驱动的机器学习算法，构建了晶体图卷积神经网络框架并应用于金属表面能预测，实现了从晶体结构到表面能的快速准确预测。使用基于物理理论的将表面尺寸与晶体的原子以及成键特征相耦合的表面表示方法时，模型表现最佳，平均绝对误差值小于0.002 eV/Å2。与第一性原理计算相比，该框架计算时间缩短了约5个数量级。最后，还进一步探讨了其在硅酸盐等复杂体系的普适性和未来的拓展方向，以期为机器学习预测表面能的提供参考。

机器学习  /  材料表面  /  表面能  /  图神经网络

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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  • 中央高校基本科研业务费(226-2022-00167)
  • 浙江大学“百人计划”
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doi: 10.14062/j.issn.0454-5648.20220802
  • 接收时间：2022-09-28
  • 首发时间：2026-10-20
  • 出版时间：2023-02-20
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  • 收稿日期：2022-09-28
  • 修回日期：2022-11-14
基金
中央高校基本科研业务费(226-2022-00167)
浙江大学“百人计划”
作者信息
    1．浙江大学材料科学与工程学院，杭州 310027
    2．唐仲英传感材料及应用研究中心，浙江大学硅材料国家重点实验室，杭州 310027

通讯作者:

吴勇军（1974—），男，博士，教授；
洪子健（1989—），男，博士，研究员。
参考文献
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2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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