Wei Deng , Yuehua Zhao , Yafang Zheng , Yuan Yin , Yan Huan , Lijun Liu , Dapeng Wang
{"title":"Machine learning assisted analysis and prediction of rubber formulation using existing databases","authors":"Wei Deng , Yuehua Zhao , Yafang Zheng , Yuan Yin , Yan Huan , Lijun Liu , Dapeng Wang","doi":"10.1016/j.aichem.2024.100054","DOIUrl":null,"url":null,"abstract":"<div><p>Designing rubber formulations can greatly benefit from using a database that stores the formulations and corresponding property data of rubber composites. Such a database can expedite the decision-making process by swiftly identifying the most suitable formulations for specific applications. However, the management of a rubber formulation database encounters various issues, including missing formulation and property data, as well as data entry errors. These issues can impede the decision-making processes and even result in incorrect decisions being made. In this study, machine learning (ML) algorithms were applied to analyze rubber formulation databases. Our findings highlight the success of the ML algorithm in effectively filling in missing data and identifying erroneous data. Furthermore, it demonstrates the accurate prediction of properties for untested formulations within the pre-determined database space. The results underline the outstanding performance of ML algorithms in expediting the rubber formulation design process and emphasize their immense potential to play a prominent role in the advancement of rubber composites.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000125/pdfft?md5=c058446a90f81b469ca59bff1d08c2a1&pid=1-s2.0-S2949747724000125-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence chemistry","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949747724000125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Designing rubber formulations can greatly benefit from using a database that stores the formulations and corresponding property data of rubber composites. Such a database can expedite the decision-making process by swiftly identifying the most suitable formulations for specific applications. However, the management of a rubber formulation database encounters various issues, including missing formulation and property data, as well as data entry errors. These issues can impede the decision-making processes and even result in incorrect decisions being made. In this study, machine learning (ML) algorithms were applied to analyze rubber formulation databases. Our findings highlight the success of the ML algorithm in effectively filling in missing data and identifying erroneous data. Furthermore, it demonstrates the accurate prediction of properties for untested formulations within the pre-determined database space. The results underline the outstanding performance of ML algorithms in expediting the rubber formulation design process and emphasize their immense potential to play a prominent role in the advancement of rubber composites.
使用可存储橡胶复合材料配方和相应属性数据的数据库,可大大有利于橡胶配方的设计。此类数据库可迅速确定最适合特定应用的配方,从而加快决策过程。然而,橡胶配方数据库的管理会遇到各种问题,包括配方和属性数据缺失以及数据输入错误。这些问题会阻碍决策过程,甚至导致做出错误的决策。本研究采用机器学习(ML)算法分析橡胶配方数据库。我们的研究结果凸显了 ML 算法在有效填补缺失数据和识别错误数据方面的成功。此外,它还证明了在预先确定的数据库空间内对未经测试的配方特性进行准确预测的能力。这些结果凸显了 ML 算法在加快橡胶配方设计过程中的出色表现,并强调了其在推动橡胶复合材料发展方面发挥突出作用的巨大潜力。