Machine learning based prediction of specific heat capacity for half-Heusler compounds
Half-Heusler alloys are among the most emerging families due to their different properties in topological insulators, superconductors, and magnetic behavior, which are directly applicable to developing low-cost and high-power spintronics devices. This study investigates the predictive performance of...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-01-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0239714 |
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| author | Laxman Chaudhary Keshab Chaudhary Ambika Shahi Kedar Nath Jaiswal Dipendra Prasad Kalauni Se-Hun Kim Madhav Prasad Ghimire |
| author_facet | Laxman Chaudhary Keshab Chaudhary Ambika Shahi Kedar Nath Jaiswal Dipendra Prasad Kalauni Se-Hun Kim Madhav Prasad Ghimire |
| author_sort | Laxman Chaudhary |
| collection | DOAJ |
| description | Half-Heusler alloys are among the most emerging families due to their different properties in topological insulators, superconductors, and magnetic behavior, which are directly applicable to developing low-cost and high-power spintronics devices. This study investigates the predictive performance of a stacked model for estimating the lattice parameters and specific heat capacity of 438 half-Heusler alloys with 28 columns in different properties. The stacked model, which incorporates gradient boosting and random forest as baseline models, was meticulously tuned for parameter optimization. Our calculated results demonstrate the robustness of our model, as evidenced by the high R-squared scores that indicate remarkable accuracy and consistency in predicting lattice parameters and specific heat capacity. The model also shows strong correlation coefficients, underscoring its reliability and precision. Comparative analysis reveals the superiority of the stacked model over alternative approaches, positioning it as the preferred model for both properties. This research highlights the stacked model’s efficacy in material property prediction, offering valuable insights for materials science research and development at a very low cost. |
| format | Article |
| id | doaj-art-338cf8fa112f4293a81670aedf90870f |
| institution | Kabale University |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-338cf8fa112f4293a81670aedf90870f2025-02-03T16:40:43ZengAIP Publishing LLCAIP Advances2158-32262025-01-01151015306015306-1010.1063/5.0239714Machine learning based prediction of specific heat capacity for half-Heusler compoundsLaxman Chaudhary0Keshab Chaudhary1Ambika Shahi2Kedar Nath Jaiswal3Dipendra Prasad Kalauni4Se-Hun Kim5Madhav Prasad Ghimire6Central Department of Physics, Tribhuvan University, Kirtipur, 44613 Kathmandu, NepalCentral Department of Physics, Tribhuvan University, Kirtipur, 44613 Kathmandu, NepalCentral Department of Physics, Tribhuvan University, Kirtipur, 44613 Kathmandu, NepalCentral Department of Physics, Tribhuvan University, Kirtipur, 44613 Kathmandu, NepalCentral Department of Physics, Tribhuvan University, Kirtipur, 44613 Kathmandu, NepalFaculty of Science Education, Jeju National University, Jeju 63243, Republic of KoreaCentral Department of Physics, Tribhuvan University, Kirtipur, 44613 Kathmandu, NepalHalf-Heusler alloys are among the most emerging families due to their different properties in topological insulators, superconductors, and magnetic behavior, which are directly applicable to developing low-cost and high-power spintronics devices. This study investigates the predictive performance of a stacked model for estimating the lattice parameters and specific heat capacity of 438 half-Heusler alloys with 28 columns in different properties. The stacked model, which incorporates gradient boosting and random forest as baseline models, was meticulously tuned for parameter optimization. Our calculated results demonstrate the robustness of our model, as evidenced by the high R-squared scores that indicate remarkable accuracy and consistency in predicting lattice parameters and specific heat capacity. The model also shows strong correlation coefficients, underscoring its reliability and precision. Comparative analysis reveals the superiority of the stacked model over alternative approaches, positioning it as the preferred model for both properties. This research highlights the stacked model’s efficacy in material property prediction, offering valuable insights for materials science research and development at a very low cost.http://dx.doi.org/10.1063/5.0239714 |
| spellingShingle | Laxman Chaudhary Keshab Chaudhary Ambika Shahi Kedar Nath Jaiswal Dipendra Prasad Kalauni Se-Hun Kim Madhav Prasad Ghimire Machine learning based prediction of specific heat capacity for half-Heusler compounds AIP Advances |
| title | Machine learning based prediction of specific heat capacity for half-Heusler compounds |
| title_full | Machine learning based prediction of specific heat capacity for half-Heusler compounds |
| title_fullStr | Machine learning based prediction of specific heat capacity for half-Heusler compounds |
| title_full_unstemmed | Machine learning based prediction of specific heat capacity for half-Heusler compounds |
| title_short | Machine learning based prediction of specific heat capacity for half-Heusler compounds |
| title_sort | machine learning based prediction of specific heat capacity for half heusler compounds |
| url | http://dx.doi.org/10.1063/5.0239714 |
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