利用机器学习模拟旋涂聚苯乙烯薄膜的体积分子量、溶液浓度和厚度之间的相互依存关系

IF 1.8 4区材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY

MRS Communications Pub Date : 2024-02-12 DOI:10.1557/s43579-024-00527-6

Alexander Chenyu Wang, Samuel Z. Chen, Evan Xie, Matthew Chang, Anthony Zhu, Adam Hansen, John Jerome, Miriam Rafailovich

{"title":"利用机器学习模拟旋涂聚苯乙烯薄膜的体积分子量、溶液浓度和厚度之间的相互依存关系","authors":"Alexander Chenyu Wang, Samuel Z. Chen, Evan Xie, Matthew Chang, Anthony Zhu, Adam Hansen, John Jerome, Miriam Rafailovich","doi":"10.1557/s43579-024-00527-6","DOIUrl":null,"url":null,"abstract":"<p>Spin coating is a quick and inexpensive method to create nanometer-thick thin films of various polymers on solid substrates. Since the film thickness determines the mechanical, optical, and degradation properties of the coating, it is essential to develop a simple method to predict thickness based on other manipulatable factors. In this study, a three-dimensional manifold simultaneously relating initial solution concentration, film thickness, and monodisperse bulk molecular weight is developed utilizing curve-fit machine learning on a dataset of spin coated polystyrene samples. Given values for any two of the three factors, the manifold presents an accurate corresponding value for the unknown.</p><h3 data-test=\"abstract-sub-heading\">Graphical abstract</h3>","PeriodicalId":19016,"journal":{"name":"MRS Communications","volume":"15 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Utilizing machine learning to model interdependency of bulk molecular weight, solution concentration, and thickness of spin coated polystyrene thin films\",\"authors\":\"Alexander Chenyu Wang, Samuel Z. Chen, Evan Xie, Matthew Chang, Anthony Zhu, Adam Hansen, John Jerome, Miriam Rafailovich\",\"doi\":\"10.1557/s43579-024-00527-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Spin coating is a quick and inexpensive method to create nanometer-thick thin films of various polymers on solid substrates. Since the film thickness determines the mechanical, optical, and degradation properties of the coating, it is essential to develop a simple method to predict thickness based on other manipulatable factors. In this study, a three-dimensional manifold simultaneously relating initial solution concentration, film thickness, and monodisperse bulk molecular weight is developed utilizing curve-fit machine learning on a dataset of spin coated polystyrene samples. Given values for any two of the three factors, the manifold presents an accurate corresponding value for the unknown.</p><h3 data-test=\\\"abstract-sub-heading\\\">Graphical abstract</h3>\",\"PeriodicalId\":19016,\"journal\":{\"name\":\"MRS Communications\",\"volume\":\"15 1\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MRS Communications\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1557/s43579-024-00527-6\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MRS Communications","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1557/s43579-024-00527-6","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

摘要

旋转涂层是在固体基底上形成纳米厚的各种聚合物薄膜的一种快速而廉价的方法。由于薄膜厚度决定了涂层的机械、光学和降解特性，因此必须开发一种简单的方法，根据其他可操作因素预测薄膜厚度。在本研究中，利用曲线拟合机器学习技术，在旋涂聚苯乙烯样品的数据集上开发出了同时与初始溶液浓度、薄膜厚度和单分散块状分子量相关的三维流形。给定三个因素中任何两个因素的值，流形就能准确地给出未知因素的相应值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Utilizing machine learning to model interdependency of bulk molecular weight, solution concentration, and thickness of spin coated polystyrene thin films

查看原文本刊更多论文

Utilizing machine learning to model interdependency of bulk molecular weight, solution concentration, and thickness of spin coated polystyrene thin films

Spin coating is a quick and inexpensive method to create nanometer-thick thin films of various polymers on solid substrates. Since the film thickness determines the mechanical, optical, and degradation properties of the coating, it is essential to develop a simple method to predict thickness based on other manipulatable factors. In this study, a three-dimensional manifold simultaneously relating initial solution concentration, film thickness, and monodisperse bulk molecular weight is developed utilizing curve-fit machine learning on a dataset of spin coated polystyrene samples. Given values for any two of the three factors, the manifold presents an accurate corresponding value for the unknown.

Graphical abstract

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

MRS Communications MATERIALS SCIENCE, MULTIDISCIPLINARY-

CiteScore

2.60

自引率

10.50%

发文量

166

审稿时长

>12 weeks

期刊介绍： MRS Communications is a full-color, high-impact journal focused on rapid publication of completed research with broad appeal to the materials community. MRS Communications offers a rapid but rigorous peer-review process and time to publication. Leveraging its access to the far-reaching technical expertise of MRS members and leading materials researchers from around the world, the journal boasts an experienced and highly respected board of principal editors and reviewers.