Exploring structural variances in monatomic metallic glasses using machine learning and molecular dynamics simulation

IF 2.1 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of Molecular Modeling Pub Date : 2024-11-12 DOI:10.1007/s00894-024-06204-8
Chengqiao Yang, Minhua Sun
{"title":"Exploring structural variances in monatomic metallic glasses using machine learning and molecular dynamics simulation","authors":"Chengqiao Yang,&nbsp;Minhua Sun","doi":"10.1007/s00894-024-06204-8","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><p>BCC and FCC metals have different glass-forming abilities (GFA) and exhibit different characteristics during the glass transition. However, the structural origin of their different GFAs is still not clear. Here, we explored the structures of eight monatomic metallic glasses by combining molecular dynamics (MD) simulations and machine learning (ML). Our findings reveal that, despite their common long-range disordered atomic structure, metallic glasses can be further classified into two distinct categories indicating an underlying structural order within the disorder. Using machine learning, we found that BCC liquids can sample more diverse glass states than FCC liquids. Furthermore, glasses formed from BCC metals (GFFBs) exhibit a higher degree of disorder than glasses formed from FCC metals (GFFFs). These findings highlight the inherent differences between GFFFs and GFFBs, which help explain the different glass-forming abilities of FCC and BCC metals. Additionally, our results demonstrate the promising potential of computer vision and ML methods in exploring material structures.</p><h3>Method</h3><p>Classical molecular dynamics simulations were employed to generate configurations of GFFBs and GFFFs, and the simulations were performed using the LAMMPS code. Inter-atomic interactions were described using a classical embedded atom model (EAM) potential. The initial configuration of the model consists of 32,000 atoms in a three-dimensional (3D) cubic box with periodic boundary conditions applied in all three directions. For machine learning, we utilized an unsupervised machine learning method along with MobileNetV2 for classifying glass structures. Image entropy and image distances were used to measure the structural differences of the metallic glasses.</p></div>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"30 12","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Modeling","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s00894-024-06204-8","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Context

BCC and FCC metals have different glass-forming abilities (GFA) and exhibit different characteristics during the glass transition. However, the structural origin of their different GFAs is still not clear. Here, we explored the structures of eight monatomic metallic glasses by combining molecular dynamics (MD) simulations and machine learning (ML). Our findings reveal that, despite their common long-range disordered atomic structure, metallic glasses can be further classified into two distinct categories indicating an underlying structural order within the disorder. Using machine learning, we found that BCC liquids can sample more diverse glass states than FCC liquids. Furthermore, glasses formed from BCC metals (GFFBs) exhibit a higher degree of disorder than glasses formed from FCC metals (GFFFs). These findings highlight the inherent differences between GFFFs and GFFBs, which help explain the different glass-forming abilities of FCC and BCC metals. Additionally, our results demonstrate the promising potential of computer vision and ML methods in exploring material structures.

Method

Classical molecular dynamics simulations were employed to generate configurations of GFFBs and GFFFs, and the simulations were performed using the LAMMPS code. Inter-atomic interactions were described using a classical embedded atom model (EAM) potential. The initial configuration of the model consists of 32,000 atoms in a three-dimensional (3D) cubic box with periodic boundary conditions applied in all three directions. For machine learning, we utilized an unsupervised machine learning method along with MobileNetV2 for classifying glass structures. Image entropy and image distances were used to measure the structural differences of the metallic glasses.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用机器学习和分子动力学模拟探索单原子金属玻璃的结构差异
背景BCC 和 FCC 金属具有不同的玻璃化能力(GFA),并在玻璃化转变过程中表现出不同的特性。然而,它们不同玻璃化能力的结构起源仍不清楚。在此,我们结合分子动力学(MD)模拟和机器学习(ML)探索了八种单原子金属玻璃的结构。我们的研究结果表明,尽管金属玻璃具有共同的长程无序原子结构,但它们可以进一步分为两个不同的类别,这表明在无序结构中存在潜在的结构秩序。利用机器学习,我们发现 BCC 液体比 FCC 液体能采样出更多样的玻璃态。此外,BCC 金属形成的玻璃(GFFBs)比 FCC 金属形成的玻璃(GFFFs)表现出更高的无序度。这些发现凸显了 GFFFs 和 GFFBs 之间的内在差异,有助于解释 FCC 和 BCC 金属形成玻璃的不同能力。此外,我们的研究结果还证明了计算机视觉和 ML 方法在探索材料结构方面的巨大潜力。方法采用经典分子动力学模拟生成 GFFB 和 GFFFs 的构型,并使用 LAMMPS 代码进行模拟。原子间的相互作用使用经典的嵌入式原子模型(EAM)势来描述。模型的初始配置包括三维(3D)立方体盒中的 32,000 个原子,在所有三个方向上都应用了周期性边界条件。在机器学习方面,我们利用无监督机器学习方法和 MobileNetV2 对玻璃结构进行分类。图像熵和图像距离用于测量金属眼镜的结构差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Molecular Modeling
Journal of Molecular Modeling 化学-化学综合
CiteScore
3.50
自引率
4.50%
发文量
362
审稿时长
2.9 months
期刊介绍: The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling. Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry. Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.
期刊最新文献
Insight into the structural and dynamic properties of novel HSP90 inhibitors through DFT calculations and molecular dynamics simulations Improved energy equations and thermal functions for diatomic molecules: a generalized fractional derivative approach NO2 properties that affect its reaction with pristine and Pt-doped SnS2: a gas sensor study Theoretical study of the synergistic effect between glyceryl monooleate lubricant and carboxymethylcellulose in reducing the coefficient of friction of water-based drilling fluids Constructing, in silico, molecular self-aggregates and micro-hydrated complexes of oxirene and thiirene
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1