A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti2SnN
To explore the MAX phase with experimental value over a wider range, a data-driven machine learning (ML) model was trained to rapidly predict the stability of MAX phases via a random forest classifier (RFC), support vector machine (SVM), and gradient boosting tree (GBT), where the deemed significant...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2025-04-01
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| Series: | Journal of Advanced Ceramics |
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| Online Access: | https://www.sciopen.com/article/10.26599/JAC.2025.9221050 |
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| author | Zhiyao Lu Yun Fan Zhaoxu Sun Xiaodong He Chuchu Yang Hang Yin Jinze Zhang Guangping Song Yongting Zheng Yuelei Bai |
| author_facet | Zhiyao Lu Yun Fan Zhaoxu Sun Xiaodong He Chuchu Yang Hang Yin Jinze Zhang Guangping Song Yongting Zheng Yuelei Bai |
| author_sort | Zhiyao Lu |
| collection | DOAJ |
| description | To explore the MAX phase with experimental value over a wider range, a data-driven machine learning (ML) model was trained to rapidly predict the stability of MAX phases via a random forest classifier (RFC), support vector machine (SVM), and gradient boosting tree (GBT), where the deemed significant descriptors were compiled from the literature and the stability of 1804 combinations of MAX phases was collected. Using this well-trained model, 190 new MAX phases were screened from 4347 MAX phases, 150 of which met the criteria for thermodynamic and intrinsic stability on the basis of first-principles calculations. Additionally, with the help of the ML model, the mean number of valence electrons and the valence electron deviation are the two most critical factors influencing stability. Additionally, one of these predicted MAX phases, Ti₂SnN, was experimentally synthesized through Lewis acid substitution reactions at 750 °C, with interesting A-site deintercalation and self-extrusion. First-principles calculations revealed that Ti₂SnN has lower elastic properties, higher damage tolerance and fracture toughness, and a higher coefficient of thermal expansion (CTE). |
| format | Article |
| id | doaj-art-35ad22ebdf494a35afdf86c3dfa4fc2a |
| institution | DOAJ |
| issn | 2226-4108 2227-8508 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Advanced Ceramics |
| spelling | doaj-art-35ad22ebdf494a35afdf86c3dfa4fc2a2025-08-20T03:19:08ZengTsinghua University PressJournal of Advanced Ceramics2226-41082227-85082025-04-01144922105010.26599/JAC.2025.9221050A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti2SnNZhiyao Lu0Yun Fan1Zhaoxu Sun2Xiaodong He3Chuchu Yang4Hang Yin5Jinze Zhang6Guangping Song7Yongting Zheng8Yuelei Bai9National Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, ChinaNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, ChinaNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, ChinaNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, ChinaNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, ChinaNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, ChinaNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, ChinaNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, ChinaNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, ChinaNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, ChinaTo explore the MAX phase with experimental value over a wider range, a data-driven machine learning (ML) model was trained to rapidly predict the stability of MAX phases via a random forest classifier (RFC), support vector machine (SVM), and gradient boosting tree (GBT), where the deemed significant descriptors were compiled from the literature and the stability of 1804 combinations of MAX phases was collected. Using this well-trained model, 190 new MAX phases were screened from 4347 MAX phases, 150 of which met the criteria for thermodynamic and intrinsic stability on the basis of first-principles calculations. Additionally, with the help of the ML model, the mean number of valence electrons and the valence electron deviation are the two most critical factors influencing stability. Additionally, one of these predicted MAX phases, Ti₂SnN, was experimentally synthesized through Lewis acid substitution reactions at 750 °C, with interesting A-site deintercalation and self-extrusion. First-principles calculations revealed that Ti₂SnN has lower elastic properties, higher damage tolerance and fracture toughness, and a higher coefficient of thermal expansion (CTE).https://www.sciopen.com/article/10.26599/JAC.2025.9221050max phasesmachine learning (ml)ti2snnfirst principlesself-extrusion |
| spellingShingle | Zhiyao Lu Yun Fan Zhaoxu Sun Xiaodong He Chuchu Yang Hang Yin Jinze Zhang Guangping Song Yongting Zheng Yuelei Bai A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti2SnN Journal of Advanced Ceramics max phases machine learning (ml) ti2snn first principles self-extrusion |
| title | A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti2SnN |
| title_full | A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti2SnN |
| title_fullStr | A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti2SnN |
| title_full_unstemmed | A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti2SnN |
| title_short | A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti2SnN |
| title_sort | fast composition stability machine learning model for screening max phases and guiding discovery of ti2snn |
| topic | max phases machine learning (ml) ti2snn first principles self-extrusion |
| url | https://www.sciopen.com/article/10.26599/JAC.2025.9221050 |
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