Machine learning potential for Ab Initio phase transitions of zirconia
Zirconia has been extensively used in aerospace, military, biomedical and industrial fields due to its unusual combination of high mechanical, electrical and thermal properties. However, the fundamental and critical phase transition process of zirconia has not been well studied because of its diffic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Theoretical and Applied Mechanics Letters |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095034923000521 |
| _version_ | 1827400964229824512 |
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| author | Yuanpeng Deng Chong Wang Xiang Xu Hui Li |
| author_facet | Yuanpeng Deng Chong Wang Xiang Xu Hui Li |
| author_sort | Yuanpeng Deng |
| collection | DOAJ |
| description | Zirconia has been extensively used in aerospace, military, biomedical and industrial fields due to its unusual combination of high mechanical, electrical and thermal properties. However, the fundamental and critical phase transition process of zirconia has not been well studied because of its difficult first-order phase transition with formidable energy barrier. Here, we generated a machine learning interatomic potential with ab initio accuracy to discover the mechanism behind all kinds of phase transition of zirconia at ambient pressure. The machine learning potential precisely characterized atomic interactions among all zirconia allotropes and liquid zirconia in a wide temperature range. We realized the challenging reversible first-order monoclinic-tetragonal and cubic-liquid phase transition processes with enhanced sampling techniques. From the thermodynamic information, we gave a better understanding of the thermal hysteresis phenomenon in martensitic monoclinic-tetragonal transition. The phase diagram of zirconia from our machine learning potential based molecular dynamics simulations corresponded well with experimental results. |
| first_indexed | 2024-03-08T20:12:19Z |
| format | Article |
| id | doaj.art-ad199f35025f4a49a786351d7f725cc4 |
| institution | Directory Open Access Journal |
| issn | 2095-0349 |
| language | English |
| last_indexed | 2024-03-08T20:12:19Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Theoretical and Applied Mechanics Letters |
| spelling | doaj.art-ad199f35025f4a49a786351d7f725cc42023-12-23T05:20:32ZengElsevierTheoretical and Applied Mechanics Letters2095-03492023-11-01136100481Machine learning potential for Ab Initio phase transitions of zirconiaYuanpeng Deng0Chong Wang1Xiang Xu2Hui Li3Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology and Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, ChinaKey Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology and Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, ChinaCorresponding authors.; Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology and Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, ChinaCorresponding authors.; Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology and Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, ChinaZirconia has been extensively used in aerospace, military, biomedical and industrial fields due to its unusual combination of high mechanical, electrical and thermal properties. However, the fundamental and critical phase transition process of zirconia has not been well studied because of its difficult first-order phase transition with formidable energy barrier. Here, we generated a machine learning interatomic potential with ab initio accuracy to discover the mechanism behind all kinds of phase transition of zirconia at ambient pressure. The machine learning potential precisely characterized atomic interactions among all zirconia allotropes and liquid zirconia in a wide temperature range. We realized the challenging reversible first-order monoclinic-tetragonal and cubic-liquid phase transition processes with enhanced sampling techniques. From the thermodynamic information, we gave a better understanding of the thermal hysteresis phenomenon in martensitic monoclinic-tetragonal transition. The phase diagram of zirconia from our machine learning potential based molecular dynamics simulations corresponded well with experimental results.http://www.sciencedirect.com/science/article/pii/S2095034923000521Machine learningMolecular dynamicsEnhanced samplingPhase transitionZirconia |
| spellingShingle | Yuanpeng Deng Chong Wang Xiang Xu Hui Li Machine learning potential for Ab Initio phase transitions of zirconia Theoretical and Applied Mechanics Letters Machine learning Molecular dynamics Enhanced sampling Phase transition Zirconia |
| title | Machine learning potential for Ab Initio phase transitions of zirconia |
| title_full | Machine learning potential for Ab Initio phase transitions of zirconia |
| title_fullStr | Machine learning potential for Ab Initio phase transitions of zirconia |
| title_full_unstemmed | Machine learning potential for Ab Initio phase transitions of zirconia |
| title_short | Machine learning potential for Ab Initio phase transitions of zirconia |
| title_sort | machine learning potential for ab initio phase transitions of zirconia |
| topic | Machine learning Molecular dynamics Enhanced sampling Phase transition Zirconia |
| url | http://www.sciencedirect.com/science/article/pii/S2095034923000521 |
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