Machine learning-driven insights into phase prediction for high entropy alloys
The unique properties of high-entropy alloys (HEAs) have attracted considerable attention, largely due to their dependence on the choice among three distinct phases: solid solution (SS), intermetallic compound (IM), or a blend of both (SS + IM). For this reason, precise phase prediction is key to id...
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Elsevier
2024-12-01
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| Series: | Journal of Alloys and Metallurgical Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949917824000579 |
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| author | Reliance Jain Sandeep Jain Sheetal Kumar Dewangan Lokesh Kumar Boriwal Sumanta Samal |
| author_facet | Reliance Jain Sandeep Jain Sheetal Kumar Dewangan Lokesh Kumar Boriwal Sumanta Samal |
| author_sort | Reliance Jain |
| collection | DOAJ |
| description | The unique properties of high-entropy alloys (HEAs) have attracted considerable attention, largely due to their dependence on the choice among three distinct phases: solid solution (SS), intermetallic compound (IM), or a blend of both (SS + IM). For this reason, precise phase prediction is key to identifying the optimal element combinations needed to develop HEAs with the required characteristics. Due to large compositional domain of HEAs is opportune to design new HEAs with desired output. A machine learning tool is exploited to discover and characterize high entropy alloys with satisfying targets. Herein, a method of designing substitutional high entropy alloys with optimization of input features and predict their phase formation, using different ML algorithms are proposed. The ML models such as multi layer precreptron MLP, Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), KNN, XGB nad SVM Classifier algorithm were used for the identifying the phase of HEAs. After assessing the accuracy and tuning of each model, an random forest classifier (accuracy = 0.914. precision = 0.916, ROC-AUC score = 0.97) model showed the best predictive capabilities for phase prediction. The new HEA was designed based on prediction and successfully validated with thermodynamic simulation. Data Availability: Data will be made available on request |
| format | Article |
| id | doaj-art-b5886c8acbb04e7f98129b23b4d4c461 |
| institution | OA Journals |
| issn | 2949-9178 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Alloys and Metallurgical Systems |
| spelling | doaj-art-b5886c8acbb04e7f98129b23b4d4c4612025-08-20T01:56:49ZengElsevierJournal of Alloys and Metallurgical Systems2949-91782024-12-01810011010.1016/j.jalmes.2024.100110Machine learning-driven insights into phase prediction for high entropy alloysReliance Jain0Sandeep Jain1Sheetal Kumar Dewangan2Lokesh Kumar Boriwal3Sumanta Samal4Department of Robotics and Automation, Prestige Institute of Engineering Management and Research, Indore, Madhya Pradesh 452010, India; Corresponding author.Department of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea; Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology Indore, Simrol, Indore, Madhya Pradesh 453552, India; Corresponding author at: Department of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaDepartment of Materials Science and Engineering, Ajou University, Suwon 16499, Republic of KoreaDepartment of Mechanical Engineering, Prestige Institute of Engineering Management and Research, Indore, Madhya Pradesh 452010, IndiaDepartment of Metallurgical Engineering and Materials Science, Indian Institute of Technology Indore, Simrol, Indore, Madhya Pradesh 453552, IndiaThe unique properties of high-entropy alloys (HEAs) have attracted considerable attention, largely due to their dependence on the choice among three distinct phases: solid solution (SS), intermetallic compound (IM), or a blend of both (SS + IM). For this reason, precise phase prediction is key to identifying the optimal element combinations needed to develop HEAs with the required characteristics. Due to large compositional domain of HEAs is opportune to design new HEAs with desired output. A machine learning tool is exploited to discover and characterize high entropy alloys with satisfying targets. Herein, a method of designing substitutional high entropy alloys with optimization of input features and predict their phase formation, using different ML algorithms are proposed. The ML models such as multi layer precreptron MLP, Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), KNN, XGB nad SVM Classifier algorithm were used for the identifying the phase of HEAs. After assessing the accuracy and tuning of each model, an random forest classifier (accuracy = 0.914. precision = 0.916, ROC-AUC score = 0.97) model showed the best predictive capabilities for phase prediction. The new HEA was designed based on prediction and successfully validated with thermodynamic simulation. Data Availability: Data will be made available on requesthttp://www.sciencedirect.com/science/article/pii/S2949917824000579High entropy alloysPhase predictionMachine learning. Model validationNew alloy design |
| spellingShingle | Reliance Jain Sandeep Jain Sheetal Kumar Dewangan Lokesh Kumar Boriwal Sumanta Samal Machine learning-driven insights into phase prediction for high entropy alloys Journal of Alloys and Metallurgical Systems High entropy alloys Phase prediction Machine learning. Model validation New alloy design |
| title | Machine learning-driven insights into phase prediction for high entropy alloys |
| title_full | Machine learning-driven insights into phase prediction for high entropy alloys |
| title_fullStr | Machine learning-driven insights into phase prediction for high entropy alloys |
| title_full_unstemmed | Machine learning-driven insights into phase prediction for high entropy alloys |
| title_short | Machine learning-driven insights into phase prediction for high entropy alloys |
| title_sort | machine learning driven insights into phase prediction for high entropy alloys |
| topic | High entropy alloys Phase prediction Machine learning. Model validation New alloy design |
| url | http://www.sciencedirect.com/science/article/pii/S2949917824000579 |
| work_keys_str_mv | AT reliancejain machinelearningdriveninsightsintophasepredictionforhighentropyalloys AT sandeepjain machinelearningdriveninsightsintophasepredictionforhighentropyalloys AT sheetalkumardewangan machinelearningdriveninsightsintophasepredictionforhighentropyalloys AT lokeshkumarboriwal machinelearningdriveninsightsintophasepredictionforhighentropyalloys AT sumantasamal machinelearningdriveninsightsintophasepredictionforhighentropyalloys |