Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94229
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Mechanical Engineering | en_US |
dc.creator | Xiong, J | en_US |
dc.creator | Shi, SQ | en_US |
dc.creator | Zhang, TY | en_US |
dc.date.accessioned | 2022-08-11T01:09:27Z | - |
dc.date.available | 2022-08-11T01:09:27Z | - |
dc.identifier.issn | 2238-7854 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/94229 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Editora Ltda | en_US |
dc.rights | © 2021 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology. | en_US |
dc.rights | © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Xiong, J., et al. (2021). "Machine learning of phases and mechanical properties in complex concentrated alloys." Journal of Materials Science & Technology 87: 133-142 is available at https://dx.doi.org/10.1016/j.jmst.2021.01.054. | en_US |
dc.subject | Complex concentrated alloys | en_US |
dc.subject | High entropy alloys | en_US |
dc.subject | Materials informatics | en_US |
dc.subject | SHAP | en_US |
dc.title | Machine learning of phases and mechanical properties in complex concentrated alloys | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 133 | en_US |
dc.identifier.epage | 142 | en_US |
dc.identifier.volume | 87 | en_US |
dc.identifier.doi | 10.1016/j.jmst.2021.01.054 | en_US |
dcterms.abstract | The mechanical properties of complex concentrated alloys (CCAs) depend on their formed phases and corresponding microstructures. The data-driven prediction of the phase formation and associated mechanical properties is essential to discovering novel CCAs. The present work collects 557 samples of various chemical compositions, comprising 61 amorphous, 167 single-phase crystalline, and 329 multi-phases crystalline CCAs. Three classification models are developed with high accuracies to category and understand the formed phases of CCAs. Also, two regression models are constructed to predict the hardness and ultimate tensile strength of CCAs, and the correlation coefficient of the random forest regression model is greater than 0.9 for both of two targeted properties. Furthermore, the Shapley additive explanation (SHAP) values are calculated, and accordingly four most important features are identified. A significant finding in the SHAP values is that there exists a critical value in each of the top four features, which provides an easy and fast assessment in the design of improved mechanical properties of CCAs. The present work demonstrates the great potential of machine learning in the design of advanced CCAs. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of materials science and technology (Brazil), 10 Oct. 2021, v. 87, p. 133-142 | en_US |
dcterms.isPartOf | Journal of materials science and technology (Brazil) | en_US |
dcterms.issued | 2021-10-10 | - |
dc.identifier.scopus | 2-s2.0-85103102123 | - |
dc.identifier.eissn | 2214-0697 | en_US |
dc.description.validate | 202208 bchy | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | ME-0017 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | the National Key R&D Program of China; the Hong Kong Polytechnic University; the 111 Project of the State Administration of Foreign Experts Affairs and the Ministry of Education, China | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 47524972 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Xiong_Machine_Learning_Phases.pdf | Pre-Published version | 1.99 MB | Adobe PDF | View/Open |
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