Bibliographic Details
| Title: |
Phase transition mechanism and property prediction of hafnium oxide-based antiferroelectric materials revealed by artificial intelligence |
| Authors: |
Shaoan Yan, Pei Xu, Gang Li, Yingfang Zhu, Yujie Wu, Qilai Chen, Sen Liu, Qingjiang Li, Minghua Tang |
| Source: |
Journal of Materiomics, Vol 11, Iss 4, Pp 100968- (2025) |
| Publisher Information: |
Elsevier, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Materials of engineering and construction. Mechanics of materials |
| Subject Terms: |
Antiferroelectric materials, Machine learning, Critical electric field, First-principle calculations, Phase transition, Materials of engineering and construction. Mechanics of materials, TA401-492 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2352-8478 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2352847824002077; https://doaj.org/toc/2352-8478 |
| DOI: |
10.1016/j.jmat.2024.100968 |
| Access URL: |
https://doaj.org/article/df3b4e6b42124e15b36f76740fcdde26 |
| Accession Number: |
edsdoj.f3b4e6b42124e15b36f76740fcdde26 |
| Database: |
Directory of Open Access Journals |
