Accelerated design of chalcogenide glasses through interpretable machine learning for composition–property relationships

IF 2.9 4区 物理与天体物理 Q2 OPTICS Journal of Nonlinear Optical Physics & Materials Pub Date : 2022-11-01 DOI:10.1088/2515-7639/acc6f2
Sayam Singla, Sajid Mannan, Mohd Zaki, N. Krishnan
{"title":"Accelerated design of chalcogenide glasses through interpretable machine learning for composition–property relationships","authors":"Sayam Singla, Sajid Mannan, Mohd Zaki, N. Krishnan","doi":"10.1088/2515-7639/acc6f2","DOIUrl":null,"url":null,"abstract":"Chalcogenide glasses (ChGs) possess various outstanding properties enabling essential applications, such as optical discs, infrared cameras, and thermal imaging systems. Despite their ubiquitous usage, these materials’ composition–property relationships remain poorly understood, impeding the pace of their discovery. Here, we use a large experimental dataset comprising ∼24 000 glass compositions made of 51 distinct elements from the periodic table to develop machine learning (ML) models for predicting 12 properties, namely, annealing point, bulk modulus, density, Vickers hardness, Littleton point, Young’s modulus, shear modulus, softening point, thermal expansion coefficient, glass transition temperature, liquidus temperature, and refractive index. These models are the largest regarding the compositional space and the number of properties covered for ChGs. Further, we use Shapley additive explanations, a game theory-based algorithm, to explain the properties’ compositional control by quantifying each element’s role toward model predictions. This work provides a powerful tool for interpreting the model’s prediction and designing new ChG compositions with targeted properties. Finally, using the trained ML models, we develop several glass-selection charts that can potentially aid in the rational design of novel ChGs for various applications.","PeriodicalId":16520,"journal":{"name":"Journal of Nonlinear Optical Physics & Materials","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nonlinear Optical Physics & Materials","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/2515-7639/acc6f2","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 1

Abstract

Chalcogenide glasses (ChGs) possess various outstanding properties enabling essential applications, such as optical discs, infrared cameras, and thermal imaging systems. Despite their ubiquitous usage, these materials’ composition–property relationships remain poorly understood, impeding the pace of their discovery. Here, we use a large experimental dataset comprising ∼24 000 glass compositions made of 51 distinct elements from the periodic table to develop machine learning (ML) models for predicting 12 properties, namely, annealing point, bulk modulus, density, Vickers hardness, Littleton point, Young’s modulus, shear modulus, softening point, thermal expansion coefficient, glass transition temperature, liquidus temperature, and refractive index. These models are the largest regarding the compositional space and the number of properties covered for ChGs. Further, we use Shapley additive explanations, a game theory-based algorithm, to explain the properties’ compositional control by quantifying each element’s role toward model predictions. This work provides a powerful tool for interpreting the model’s prediction and designing new ChG compositions with targeted properties. Finally, using the trained ML models, we develop several glass-selection charts that can potentially aid in the rational design of novel ChGs for various applications.
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
通过可解释的机器学习来加速设计硫系玻璃的组成-性质关系
硫系玻璃(ChGs)具有多种优异的性能,可用于光盘、红外相机和热成像系统等重要应用。尽管这些材料被广泛使用,但人们对它们的组成-性质关系仍然知之甚少,这阻碍了它们的发现步伐。在这里,我们使用一个大型实验数据集,包括由元素周期表中的51种不同元素组成的约24000种玻璃成分,以开发机器学习(ML)模型来预测12种性质,即退火点、体积模量、密度、维氏硬度、利特尔顿点、杨氏模量、剪切模量、软化点、热膨胀系数、玻璃转变温度、液相温度和折射率。这些模型在组成空间和chg覆盖的属性数量方面是最大的。此外,我们使用Shapley加性解释,一种基于博弈论的算法,通过量化每个元素对模型预测的作用来解释属性的组成控制。这项工作为解释模型预测和设计具有目标性质的新ChG组合物提供了有力的工具。最后,使用训练好的ML模型,我们开发了几个玻璃选择图表,这些图表可能有助于为各种应用合理设计新型chg。
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
CiteScore
3.00
自引率
48.10%
发文量
53
审稿时长
3 months
期刊介绍: This journal is devoted to the rapidly advancing research and development in the field of nonlinear interactions of light with matter. Topics of interest include, but are not limited to, nonlinear optical materials, metamaterials and plasmonics, nano-photonic structures, stimulated scatterings, harmonic generations, wave mixing, real time holography, guided waves and solitons, bistabilities, instabilities and nonlinear dynamics, and their applications in laser and coherent lightwave amplification, guiding, switching, modulation, communication and information processing. Original papers, comprehensive reviews and rapid communications reporting original theories and observations are sought for in these and related areas. This journal will also publish proceedings of important international meetings and workshops. It is intended for graduate students, scientists and researchers in academic, industrial and government research institutions.
期刊最新文献
Dominance of polarization modes and absorption on self-focusing of laser beams in collisionless magnetized plasma Influence of the focusing parameter on the SHG efficiency at a wavelength of 968 nm in a ZnSe polycrystal Linear and nonlinear electric and magneto-optical absorption coefficients and relative refractive index changes in WxMo1-xS2 Monolayer: Role of Tungsten contents Optical properties of silver-doped ZnS nanostructures W-chirped solitons and modulated waves patterns in parabolic law medium with anti-cubic nonlinearity
×
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