A deep-neural network potential to study transformation-induced plasticity in zirconia.

Zirconia (ZrO 2) ceramics uniquely exhibit transformation-induced plasticity, allowing plastic deformation prior to failure, setting them apart from most other ceramics. However, our understanding of ZrO 2 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 ZrO 2. In this work, we introduce a novel deep neural network interatomic potential, DP-ZrO 2 , constructed using a concurrent-learning approach. DP-ZrO 2 reproduces properties of various ZrO 2 phases, matching their phase diagrams as well as transformation pathways with accuracy comparable to ab initio density functional theory. Leveraging DP-ZrO 2 , 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 ZrO 2. Our approach opens the door to large-scale simulations under complex loading conditions, which will shed light on the conditions favouring ZrO 2 transformation-induced plasticity. [ABSTRACT FROM AUTHOR]