Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106431
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Mechanical Engineering | - |
dc.creator | Xiong, J | - |
dc.creator | Zhang, TY | - |
dc.creator | Shi, SQ | - |
dc.date.accessioned | 2024-05-09T00:53:30Z | - |
dc.date.available | 2024-05-09T00:53:30Z | - |
dc.identifier.issn | 2159-6859 | - |
dc.identifier.uri | http://hdl.handle.net/10397/106431 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.rights | © Materials Research Society, 2019 | en_US |
dc.rights | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use(https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1557/mrc.2019.44. | en_US |
dc.title | Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 576 | - |
dc.identifier.epage | 585 | - |
dc.identifier.volume | 9 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.1557/mrc.2019.44 | - |
dcterms.abstract | There is a genuine need to shorten the development period for new materials with desired properties. In this work, machine learning (ML) was conducted on a dataset of the elastic moduli of 219 bulk-metallic glasses (BMGs) and another dataset of the critical casting diameters (Dmax) of 442 BMGs. The resulting ML model predicted the moduli and Dmax of BMGs in good agreement with most experimentally measured values, and the model even identified some errors reported in the literature. This work indicates the great potential of ML in design of advanced materials with target properties. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | MRS communications, June 2019, v. 9, no. 2, p. 576-585 | - |
dcterms.isPartOf | MRS communications | - |
dcterms.issued | 2019-06 | - |
dc.identifier.scopus | 2-s2.0-85064868739 | - |
dc.identifier.eissn | 2159-6867 | - |
dc.description.validate | 202405 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | ME-0449 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | the PolyU internal grants; NSFC; the Science and Technology Commission of Shanghai Municipality; the 111 Project from the State Administration of Foreign Experts Affairs and the Ministry of Education, PRC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20272276 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
---|---|---|---|---|
Xiong_Machine_Learning_Prediction.pdf | Pre-Published version | 803.12 kB | Adobe PDF | View/Open |
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