Design of high bulk moduli high entropy alloys using machine learning
In this work, the authors have demonstrated the use of machine learning (ML) models in the prediction of bulk modulus for High Entropy Alloys (HEA). For the first time, ML has been used for optimizing the composition of HEA to achieve enhanced bulk modulus values. A total of 12 ML algorithms were tr...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
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2024
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| Online Access: | https://hdl.handle.net/10356/173853 |
| _version_ | 1826114389251457024 |
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| author | Kandavalli, Manjunadh Agarwal, Abhishek Poonia, Ansh Kishor, Modalavalasa Ayyagari, Kameswari Prasada Rao |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Kandavalli, Manjunadh Agarwal, Abhishek Poonia, Ansh Kishor, Modalavalasa Ayyagari, Kameswari Prasada Rao |
| author_sort | Kandavalli, Manjunadh |
| collection | NTU |
| description | In this work, the authors have demonstrated the use of machine learning (ML) models in the prediction of bulk modulus for High Entropy Alloys (HEA). For the first time, ML has been used for optimizing the composition of HEA to achieve enhanced bulk modulus values. A total of 12 ML algorithms were trained to classify the elemental composition as HEA or non-HEA. Among these models, Gradient Boosting Classifier (GBC) was found to be the most accurate, with a test accuracy of 78%. Further, six regression models were trained to predict the bulk modulus of HEAs, and the best results were obtained by LASSO Regression model with an R-square value of 0.98 and an adjusted R-Square value of 0.97 for the test data set. This work effectively bridges the gap in the discovery and property analysis of HEAs. By accelerating material discovery via providing alternate means for designing virtual alloy compositions having favourable bulk modulus for respective applications, this work opens new avenues of applications of HEAs. |
| first_indexed | 2024-10-01T03:38:42Z |
| format | Journal Article |
| id | ntu-10356/173853 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:38:42Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1738532024-03-08T15:35:57Z Design of high bulk moduli high entropy alloys using machine learning Kandavalli, Manjunadh Agarwal, Abhishek Poonia, Ansh Kishor, Modalavalasa Ayyagari, Kameswari Prasada Rao School of Computer Science and Engineering Engineering Least absolute shrinkage and selection operator Machine learning In this work, the authors have demonstrated the use of machine learning (ML) models in the prediction of bulk modulus for High Entropy Alloys (HEA). For the first time, ML has been used for optimizing the composition of HEA to achieve enhanced bulk modulus values. A total of 12 ML algorithms were trained to classify the elemental composition as HEA or non-HEA. Among these models, Gradient Boosting Classifier (GBC) was found to be the most accurate, with a test accuracy of 78%. Further, six regression models were trained to predict the bulk modulus of HEAs, and the best results were obtained by LASSO Regression model with an R-square value of 0.98 and an adjusted R-Square value of 0.97 for the test data set. This work effectively bridges the gap in the discovery and property analysis of HEAs. By accelerating material discovery via providing alternate means for designing virtual alloy compositions having favourable bulk modulus for respective applications, this work opens new avenues of applications of HEAs. Published version 2024-03-04T00:45:15Z 2024-03-04T00:45:15Z 2023 Journal Article Kandavalli, M., Agarwal, A., Poonia, A., Kishor, M. & Ayyagari, K. P. R. (2023). Design of high bulk moduli high entropy alloys using machine learning. Scientific Reports, 13(1), 20504-. https://dx.doi.org/10.1038/s41598-023-47181-x 2045-2322 https://hdl.handle.net/10356/173853 10.1038/s41598-023-47181-x 37993607 2-s2.0-85177699060 1 13 20504 en Scientific Reports © The Author(s) 2023. Open Access. 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. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. application/pdf |
| spellingShingle | Engineering Least absolute shrinkage and selection operator Machine learning Kandavalli, Manjunadh Agarwal, Abhishek Poonia, Ansh Kishor, Modalavalasa Ayyagari, Kameswari Prasada Rao Design of high bulk moduli high entropy alloys using machine learning |
| title | Design of high bulk moduli high entropy alloys using machine learning |
| title_full | Design of high bulk moduli high entropy alloys using machine learning |
| title_fullStr | Design of high bulk moduli high entropy alloys using machine learning |
| title_full_unstemmed | Design of high bulk moduli high entropy alloys using machine learning |
| title_short | Design of high bulk moduli high entropy alloys using machine learning |
| title_sort | design of high bulk moduli high entropy alloys using machine learning |
| topic | Engineering Least absolute shrinkage and selection operator Machine learning |
| url | https://hdl.handle.net/10356/173853 |
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