Phase transition mechanism and property prediction of hafnium oxide-based antiferroelectric materials revealed by artificial intelligence
Constrained by the inefficiency of traditional trial-and-error methods, especially when dealing with thousands of candidate materials, the swift discovery of materials with specific properties remains a central challenge in contemporary materials research. This study employed an artificial intellige...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Journal of Materiomics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352847824002077 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849338183948435456 |
|---|---|
| author | Shaoan Yan Pei Xu Gang Li Yingfang Zhu Yujie Wu Qilai Chen Sen Liu Qingjiang Li Minghua Tang |
| author_facet | Shaoan Yan Pei Xu Gang Li Yingfang Zhu Yujie Wu Qilai Chen Sen Liu Qingjiang Li Minghua Tang |
| author_sort | Shaoan Yan |
| collection | DOAJ |
| description | Constrained by the inefficiency of traditional trial-and-error methods, especially when dealing with thousands of candidate materials, the swift discovery of materials with specific properties remains a central challenge in contemporary materials research. This study employed an artificial intelligence-driven materials design framework for identifying dopants that impart antiferroelectric properties to HfO2 materials. This strategy integrates density functional theory (DFT) with machine learning (ML) techniques to swiftly screen HfO2 materials exhibiting stable antiferroelectric properties based on the critical electric field. This approach aims to overcome the high cost and lengthy cycles associated with traditional trial-and-error and experimental methods. Among 30 undeveloped dopants, four candidate dopants demonstrating stable antiferroelectric properties were identified. Subsequent DFT analysis highlighted the Ga dopant, which displayed favorable characteristics such as a small volume change, minimal lattice deformation, and a low critical electric field after incorporation into hafnium oxide. These findings suggest the potential for stable antiferroelectric performance. Essentially, we established a correlation between the physical characteristics of hafnium oxide dopants and their antiferroelectric performance. The approach facilitates large-scale ML predictions, rendering it applicable to a broad spectrum of functional material designs. |
| format | Article |
| id | doaj-art-df3b4e6b42124e15b36f76740fcdde26 |
| institution | Kabale University |
| issn | 2352-8478 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materiomics |
| spelling | doaj-art-df3b4e6b42124e15b36f76740fcdde262025-08-20T03:44:28ZengElsevierJournal of Materiomics2352-84782025-07-0111410096810.1016/j.jmat.2024.100968Phase transition mechanism and property prediction of hafnium oxide-based antiferroelectric materials revealed by artificial intelligenceShaoan Yan0Pei Xu1Gang Li2Yingfang Zhu3Yujie Wu4Qilai Chen5Sen Liu6Qingjiang Li7Minghua Tang8School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan, 411105, Hunan, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan, 411105, Hunan, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan, 411105, Hunan, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan, 411105, Hunan, China; Corresponding author.School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan, 411105, Hunan, ChinaSchool of Materials, Sun Yat-sen University, Shenzhen, 518107, Guangdong, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, China; Corresponding author.College of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, ChinaSchool of Materials Science and Engineering, Xiangtan University, Xiangtan, 411105, Hunan, China; Corresponding author.Constrained by the inefficiency of traditional trial-and-error methods, especially when dealing with thousands of candidate materials, the swift discovery of materials with specific properties remains a central challenge in contemporary materials research. This study employed an artificial intelligence-driven materials design framework for identifying dopants that impart antiferroelectric properties to HfO2 materials. This strategy integrates density functional theory (DFT) with machine learning (ML) techniques to swiftly screen HfO2 materials exhibiting stable antiferroelectric properties based on the critical electric field. This approach aims to overcome the high cost and lengthy cycles associated with traditional trial-and-error and experimental methods. Among 30 undeveloped dopants, four candidate dopants demonstrating stable antiferroelectric properties were identified. Subsequent DFT analysis highlighted the Ga dopant, which displayed favorable characteristics such as a small volume change, minimal lattice deformation, and a low critical electric field after incorporation into hafnium oxide. These findings suggest the potential for stable antiferroelectric performance. Essentially, we established a correlation between the physical characteristics of hafnium oxide dopants and their antiferroelectric performance. The approach facilitates large-scale ML predictions, rendering it applicable to a broad spectrum of functional material designs.http://www.sciencedirect.com/science/article/pii/S2352847824002077Antiferroelectric materialsMachine learningCritical electric fieldFirst-principle calculationsPhase transition |
| spellingShingle | Shaoan Yan Pei Xu Gang Li Yingfang Zhu Yujie Wu Qilai Chen Sen Liu Qingjiang Li Minghua Tang Phase transition mechanism and property prediction of hafnium oxide-based antiferroelectric materials revealed by artificial intelligence Journal of Materiomics Antiferroelectric materials Machine learning Critical electric field First-principle calculations Phase transition |
| title | Phase transition mechanism and property prediction of hafnium oxide-based antiferroelectric materials revealed by artificial intelligence |
| title_full | Phase transition mechanism and property prediction of hafnium oxide-based antiferroelectric materials revealed by artificial intelligence |
| title_fullStr | Phase transition mechanism and property prediction of hafnium oxide-based antiferroelectric materials revealed by artificial intelligence |
| title_full_unstemmed | Phase transition mechanism and property prediction of hafnium oxide-based antiferroelectric materials revealed by artificial intelligence |
| title_short | Phase transition mechanism and property prediction of hafnium oxide-based antiferroelectric materials revealed by artificial intelligence |
| title_sort | phase transition mechanism and property prediction of hafnium oxide based antiferroelectric materials revealed by artificial intelligence |
| topic | Antiferroelectric materials Machine learning Critical electric field First-principle calculations Phase transition |
| url | http://www.sciencedirect.com/science/article/pii/S2352847824002077 |
| work_keys_str_mv | AT shaoanyan phasetransitionmechanismandpropertypredictionofhafniumoxidebasedantiferroelectricmaterialsrevealedbyartificialintelligence AT peixu phasetransitionmechanismandpropertypredictionofhafniumoxidebasedantiferroelectricmaterialsrevealedbyartificialintelligence AT gangli phasetransitionmechanismandpropertypredictionofhafniumoxidebasedantiferroelectricmaterialsrevealedbyartificialintelligence AT yingfangzhu phasetransitionmechanismandpropertypredictionofhafniumoxidebasedantiferroelectricmaterialsrevealedbyartificialintelligence AT yujiewu phasetransitionmechanismandpropertypredictionofhafniumoxidebasedantiferroelectricmaterialsrevealedbyartificialintelligence AT qilaichen phasetransitionmechanismandpropertypredictionofhafniumoxidebasedantiferroelectricmaterialsrevealedbyartificialintelligence AT senliu phasetransitionmechanismandpropertypredictionofhafniumoxidebasedantiferroelectricmaterialsrevealedbyartificialintelligence AT qingjiangli phasetransitionmechanismandpropertypredictionofhafniumoxidebasedantiferroelectricmaterialsrevealedbyartificialintelligence AT minghuatang phasetransitionmechanismandpropertypredictionofhafniumoxidebasedantiferroelectricmaterialsrevealedbyartificialintelligence |