Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses
Abstract Glass-forming ability (GFA) of bulk metallic glasses (BMGs) is a determinant parameter which has been significantly studied. GFA improvements could be achieved through trial-and-error experiments, as a tedious work, or by using developed predicting tools. Machine-Learning (ML) has been used...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-15981-2 |
| _version_ | 1811294601962586112 |
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| author | Alireza Ghorbani Amirhossein Askari Mehdi Malekan Mahmoud Nili-Ahmadabadi |
| author_facet | Alireza Ghorbani Amirhossein Askari Mehdi Malekan Mahmoud Nili-Ahmadabadi |
| author_sort | Alireza Ghorbani |
| collection | DOAJ |
| description | Abstract Glass-forming ability (GFA) of bulk metallic glasses (BMGs) is a determinant parameter which has been significantly studied. GFA improvements could be achieved through trial-and-error experiments, as a tedious work, or by using developed predicting tools. Machine-Learning (ML) has been used as a promising method to predict the properties of BMGs by removing the barriers in the way of its alloy design. This article aims to develop a ML-based method for predicting the maximum critical diameter (Dmax) of BMGs as a factor of their glass-forming ability. The main result is that the random forest method can be used as a sustainable model (R 2 = 92%) for predicting glass-forming ability. Also, adding characteristic temperatures to the model will increase the accuracy and efficiency of the developed model. Comparing the measured and predicted values of Dmax for a set of newly developed BMGs indicated that the model is reliable and can be truly used for predicting the GFA of BMGs. |
| first_indexed | 2024-04-13T05:19:51Z |
| format | Article |
| id | doaj.art-0ba4268a72824186885661ef3736b2a0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T05:19:51Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0ba4268a72824186885661ef3736b2a02022-12-22T03:00:47ZengNature PortfolioScientific Reports2045-23222022-07-0112111010.1038/s41598-022-15981-2Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glassesAlireza Ghorbani0Amirhossein Askari1Mehdi Malekan2Mahmoud Nili-Ahmadabadi3School of Metallurgy and Materials Engineering, College of Engineering, University of TehranComputer Engineering Department, Amirkabir University of TechnologySchool of Metallurgy and Materials Engineering, College of Engineering, University of TehranSchool of Metallurgy and Materials Engineering, College of Engineering, University of TehranAbstract Glass-forming ability (GFA) of bulk metallic glasses (BMGs) is a determinant parameter which has been significantly studied. GFA improvements could be achieved through trial-and-error experiments, as a tedious work, or by using developed predicting tools. Machine-Learning (ML) has been used as a promising method to predict the properties of BMGs by removing the barriers in the way of its alloy design. This article aims to develop a ML-based method for predicting the maximum critical diameter (Dmax) of BMGs as a factor of their glass-forming ability. The main result is that the random forest method can be used as a sustainable model (R 2 = 92%) for predicting glass-forming ability. Also, adding characteristic temperatures to the model will increase the accuracy and efficiency of the developed model. Comparing the measured and predicted values of Dmax for a set of newly developed BMGs indicated that the model is reliable and can be truly used for predicting the GFA of BMGs.https://doi.org/10.1038/s41598-022-15981-2 |
| spellingShingle | Alireza Ghorbani Amirhossein Askari Mehdi Malekan Mahmoud Nili-Ahmadabadi Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses Scientific Reports |
| title | Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses |
| title_full | Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses |
| title_fullStr | Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses |
| title_full_unstemmed | Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses |
| title_short | Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses |
| title_sort | thermodynamically guided machine learning modelling for predicting the glass forming ability of bulk metallic glasses |
| url | https://doi.org/10.1038/s41598-022-15981-2 |
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