Show simple item record

dc.contributor.authorTian, Xiaojuan
dc.contributor.authorChen, Mingguang
dc.date.accessioned2021-01-20T08:32:31Z
dc.date.available2021-01-20T08:32:31Z
dc.date.issued2021-01-12
dc.date.submitted2020-09-12
dc.identifier.citationTian, X., & Chen, M. (2021). Descriptor selection for predicting interfacial thermal resistance by machine learning methods. Scientific Reports, 11(1). doi:10.1038/s41598-020-80795-z
dc.identifier.issn2045-2322
dc.identifier.pmid33436976
dc.identifier.doi10.1038/s41598-020-80795-z
dc.identifier.urihttp://hdl.handle.net/10754/666947
dc.description.abstractInterfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X = 20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications.
dc.description.sponsorshipWe gratefully acknowledge the financial support from National Natural Science Foundation of China (No. 21808240).
dc.publisherSpringer Nature
dc.relation.urlhttp://www.nature.com/articles/s41598-020-80795-z
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleDescriptor selection for predicting interfacial thermal resistance by machine learning methods.
dc.typeArticle
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalScientific reports
dc.identifier.pmcidPMC7804206
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Chemical Engineering, China University of Petroleum, Beijing, 102249, China
dc.identifier.volume11
dc.identifier.issue1
kaust.personChen, Mingguang
dc.date.accepted2020-12-28
dc.identifier.eid2-s2.0-85099208587
refterms.dateFOA2021-01-20T08:33:22Z
dc.date.published-online2021-01-12
dc.date.published-print2021-12


Files in this item

Thumbnail
Name:
s41598-020-80795-z.pdf
Size:
1.416Mb
Format:
PDF
Description:
Published version

This item appears in the following Collection(s)

Show simple item record

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.