所属:Osaka University
概要:Zirconia (ZrO2) ceramics uniquely exhibit transformation-induced plasticity, allowing plastic deformation prior to failure, setting them apart from most other ceramics. However, our understanding of ZrO2 plasticity is hindered by the challenge of simulating stress-induced atomic-scale phase transformations, owing to lack of an efficient interatomic potential accurately representing polymorphism and phase changes in ZrO2. In this work, we introduce a novel deep neural network interatomic potential (NNIP), constructed using a concurrent-learning approach. Our NNIP reproduces properties of various ZrO2 phases, matching their phase diagrams as well as transformation pathways with accuracy comparable to ab initio density functional theory. We conducted molecular dynamics simulations of temperature-induced interphase boundary migration and nanocompression. These simulations demonstrate the potential’s efficiency and applicability in studying deformation microstructures involving phase transformations in ZrO2. Our approach opens the door to large-scale simulations under complex loading conditions, which will shed light on the conditions favouring ZrO2 transformation-induced plasticity.
論文掲載,発表実績:
(学術雑誌掲載論文)
- Jinyu Zhang, Gaël Huynh, Fuzhi Dai, Tristan Albaret, Shihao Zhang, Shigenobu Ogata, David Rodney “A deep-neural network potential to study transformation-induced plasticity in zirconia”, Journal of the European Ceramic Society, Vol.44, Issure 1, pp.4243-4254, 2024.
Posted : 2024年03月31日