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Article(id=1319236491205788274, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319236433387303232, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220115, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1644768000000, receivedDateStr=2022-02-14, revisedDate=1645200000000, revisedDateStr=2022-02-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1792460089731, onlineDateStr=2026-10-20, pubDate=1660924800000, pubDateStr=2022-08-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792460089731, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792460089731, creator=13701087609, updateTime=1792460089731, updator=13701087609, issue=Issue{id=1319236433387303232, tenantId=1146029695717560320, journalId=1263187385517883426, year='2022', volume='50', issue='8', pageStart='2067', pageEnd='2350', issueExtLink='null', onlineDate='null', pubDate='1660924800000', pubDateStr='2022-08-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792460075946, creator='13701087609', updateTime=1792460621958, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319238723569569958, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319236433387303232, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319238723569569959, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319236433387303232, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2338, endPage=2350, ext={EN=ArticleExt(id=1319236491763630707, articleId=1319236491205788274, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Simulation Methods of Glass Composition and Properties: A Short Review, columnId=1263922772401635899, journalTitle=Journal of the Chinese Ceramic Society, columnName=Review, runingTitle=null, highlight=null, articleAbstract=

Seven methods of modeling glass properties were briefly reviewed, i.e., covering additive method, phase diagram method, Priven method, topological theory, molecular dynamics simulation, machine learning, and mathematical statistical modeling (composition-property, structure-property, and composition-structure-property). The principle, theoretical basis, procedure of each method and their application were highlighted. The additive method demonstrates its application modeling multiple glass properties. The phase diagram approach is suitable for binary, ternary and quaternary systems of silicate, borate and borosilicate glass systems. The Priven method combines glass structure, thermodynamic equations and computer simulation; topological theory is used to simulate several properties of simple oxide and sulfide glass. The molecular dynamics simulation provides insight of molecular structures of glass of various compositions. The machine learning method is utilized based on a large database from the literature to predict properties of complex glass systems. The statistical modeling methodology is applied to bridge mathematical interrelationships of composition (C) - structure (S) - property (P) of multicomponent systems of silicate, borosilicate, and phosphate glasses. The C-S-P statistical modeling approach shows the improvement in accuracy and precision rather than the conventional C-P statistical modeling in the design of new glasses.

, authors=Liyan ZHANG1, Hong LI2, Shubin CHEN1, Zhongdi LI3, Minzhi RUAN3, Tianfeng XUE1, Min QIAN1, Sijun FAN1, authorsList=Liyan ZHANG, Hong LI, Shubin CHEN, Zhongdi LI, Minzhi RUAN, Tianfeng XUE, Min QIAN, Sijun FAN, authorCompany=null, correspAuthors=null, authorNote=

ZHANG Liyan (1971-), female, Associate Professor. E-mail:

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介绍了加和法、相图法、Priven法、拓扑束缚理论、分子动力学模拟、机器学习及数理统计模拟等7种玻璃成分性质模拟方法，总结了各模拟方法的主要理论依据、模拟过程及应用现状。加和法可进行多种玻璃物理性质的预测，相图法在二、三、四元硅酸盐、硼酸盐及硼硅酸盐玻璃体系上的运用较为成熟，Priven法结合了玻璃结构、热力学方程及计算机模拟，拓扑束缚理论目前应用于氧化物和硫化物体系个别性质的模拟，分子动力学模拟亦应用于多种玻璃体系中的分子结构分析和相关性质预测，机器学习能充分利用文献中提供的大量数据来模拟复杂玻璃性质。目前数理统计模拟法已用在建立复杂玻璃体系中的成分（C）-结构（S）-性质（P）的相关数学模型，包括硅酸盐，硼硅酸盐和磷酸盐玻璃。较传统的C-P统计模拟方法，C-S-P统计模拟法能对复杂玻璃体系提供更精准的性质评估，有助于新型玻璃开发。

, authors=张丽艳1, 李洪2, 陈树彬1, 李忠镝3, 阮苠秩3, 薛天锋1, 钱敏1, 凡思军1, authorsList=张丽艳, 李洪, 陈树彬, 李忠镝, 阮苠秩, 薛天锋, 钱敏, 凡思军, authorCompany=null, correspAuthors=null, authorNote=

张丽艳（1971—），女，副研究员。

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ZHANG Liyan (1971-), female, Associate Professor. E-mail:

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张丽艳（1971—），女，副研究员。

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张丽艳（1971—），女，副研究员。

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玻璃的成分和性质的模拟方法
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张丽艳 1 , 李洪 2 , 陈树彬 1 , 李忠镝 3 , 阮苠秩 3 , 薛天锋 1 , 钱敏 1 , 凡思军 1
硅酸盐学报 | 综合评述 2022,50(8): 2338-2350
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硅酸盐学报 |综合评述 2022 , 50 (8) : 2338 -2350
玻璃的成分和性质的模拟方法
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张丽艳1 , 李洪2, 陈树彬1, 李忠镝3, 阮苠秩3, 薛天锋1, 钱敏1, 凡思军1
作者信息
  • 1．中国科学院上海光学精密机械研究所，强激光材料重点实验室，中国 上海 201800
  • 2．日本电气硝子株式会社（NEG）美国分公司，美国 北卡罗来纳 28150
  • 3．中国核电工程有限公司，中国 北京 100840
作者简介:

张丽艳（1971—），女，副研究员。

Simulation Methods of Glass Composition and Properties: A Short Review
Liyan ZHANG1 , Hong LI2, Shubin CHEN1, Zhongdi LI3, Minzhi RUAN3, Tianfeng XUE1, Min QIAN1, Sijun FAN1
Affiliations
  • 1.Key Laboratory of Materials for High Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
  • 2.Nippon Electric Glass, North Carolina 28150, USA
  • 3.China Nuclear Power Engineering Co., Ltd, Beijing 100840, China

    • ZHANG Liyan (1971-), female, Associate Professor. E-mail:

出版时间: 2022-08-20 doi: 10.14062/j.issn.0454-5648.20220115
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介绍了加和法、相图法、Priven法、拓扑束缚理论、分子动力学模拟、机器学习及数理统计模拟等7种玻璃成分性质模拟方法，总结了各模拟方法的主要理论依据、模拟过程及应用现状。加和法可进行多种玻璃物理性质的预测，相图法在二、三、四元硅酸盐、硼酸盐及硼硅酸盐玻璃体系上的运用较为成熟，Priven法结合了玻璃结构、热力学方程及计算机模拟，拓扑束缚理论目前应用于氧化物和硫化物体系个别性质的模拟，分子动力学模拟亦应用于多种玻璃体系中的分子结构分析和相关性质预测，机器学习能充分利用文献中提供的大量数据来模拟复杂玻璃性质。目前数理统计模拟法已用在建立复杂玻璃体系中的成分（C）-结构（S）-性质（P）的相关数学模型，包括硅酸盐，硼硅酸盐和磷酸盐玻璃。较传统的C-P统计模拟方法，C-S-P统计模拟法能对复杂玻璃体系提供更精准的性质评估，有助于新型玻璃开发。

加和法  /  相图法  /  Priven法  /  拓扑束缚理论  /  分子动力学模拟  /  机器学习  /  数理统计模拟

Seven methods of modeling glass properties were briefly reviewed, i.e., covering additive method, phase diagram method, Priven method, topological theory, molecular dynamics simulation, machine learning, and mathematical statistical modeling (composition-property, structure-property, and composition-structure-property). The principle, theoretical basis, procedure of each method and their application were highlighted. The additive method demonstrates its application modeling multiple glass properties. The phase diagram approach is suitable for binary, ternary and quaternary systems of silicate, borate and borosilicate glass systems. The Priven method combines glass structure, thermodynamic equations and computer simulation; topological theory is used to simulate several properties of simple oxide and sulfide glass. The molecular dynamics simulation provides insight of molecular structures of glass of various compositions. The machine learning method is utilized based on a large database from the literature to predict properties of complex glass systems. The statistical modeling methodology is applied to bridge mathematical interrelationships of composition (C) - structure (S) - property (P) of multicomponent systems of silicate, borosilicate, and phosphate glasses. The C-S-P statistical modeling approach shows the improvement in accuracy and precision rather than the conventional C-P statistical modeling in the design of new glasses.

additive method  /  phase diagram-based method  /  priven method  /  topological theory  /  molecular dynamics simulations  /  machine learning  /  mathematical statistical modeling
张丽艳, 李洪, 陈树彬, 李忠镝, 阮苠秩, 薛天锋, 钱敏, 凡思军. 玻璃的成分和性质的模拟方法. 硅酸盐学报, 2022 , 50 (8) : 2338 -2350 . DOI: 10.14062/j.issn.0454-5648.20220115
Liyan ZHANG, Hong LI, Shubin CHEN, Zhongdi LI, Minzhi RUAN, Tianfeng XUE, Min QIAN, Sijun FAN. Simulation Methods of Glass Composition and Properties: A Short Review[J]. Journal of the Chinese Ceramic Society, 2022 , 50 (8) : 2338 -2350 . DOI: 10.14062/j.issn.0454-5648.20220115
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  • 两步法冷坩埚玻璃固化工程样机研制及验证(FKY1683ZH G001SSJS-B01-001)
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2022年第50卷第8期
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doi: 10.14062/j.issn.0454-5648.20220115
  • 接收时间：2022-02-14
  • 首发时间：2026-10-20
  • 出版时间：2022-08-20
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  • 收稿日期：2022-02-14
  • 修回日期：2022-02-19
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两步法冷坩埚玻璃固化工程样机研制及验证(FKY1683ZH G001SSJS-B01-001)
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    1．中国科学院上海光学精密机械研究所，强激光材料重点实验室，中国 上海 201800
    2．日本电气硝子株式会社（NEG）美国分公司，美国 北卡罗来纳 28150
    3．中国核电工程有限公司，中国 北京 100840
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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20220115
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2种不同金属材料的力学参数
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Number of
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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
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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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