journal article

Journal Article

npj Computational Materials. 2024, 10, p. 266

Neural network potential for dislocation plasticity in ceramics

Nature Research

English

2024-11-22

https://doi.org/10.1038/s41524-024-01456-7

Zhang S., Li Y., Suzuki S., et al. Neural network potential for dislocation plasticity in ceramics. npj Computational Materials 10, 266 (2024); https://doi.org/10.1038/s41524-024-01456-7.

Dislocations in ceramics are increasingly recognized for their promising potential in applications such as toughening intrinsically brittle ceramics and tailoring functional properties. However, the atomistic simulation of dislocation plasticity in ceramics remains challenging due to the complex interatomic interactions characteristic of ceramics, which include a mix of ionic and covalent bonds, and highly distorted and extensive dislocation core structures within complex crystal structures. These complexities exceed the capabilities of empirical interatomic potentials. Therefore, constructing neural network potentials (NNPs) emerges as the optimal solution. Yet, creating a training dataset that includes dislocation structures proves difficult due to the complexity of their core configurations in ceramics and the computational demands of density functional theory for large atomic models containing dislocation cores. In this work, we propose a training dataset from properties that are easier to compute via high-throughput calculation. Using this dataset, we have successfully developed NNPs for dislocation plasticity in ceramics, specifically for three typical functional ceramics: ZnO, GaN, and SrTiO3. These NNPs effectively capture the nonstoichiometric and charged core structures and slip barriers of dislocations, as well as the long-range electrostatic interactions between charged dislocations. The effectiveness of this dataset was further validated by measuring the similarity and uncertainty across snapshots derived from large-scale simulations, alongside extensive validation across various properties. Utilizing the constructed NNPs, we examined dislocation plasticity in ceramics through nanopillar compression and nanoindentation, which demonstrated excellent agreement with experimental observations. This study provides an effective framework for constructing NNPs that enable the detailed atomistic modeling of dislocation plasticity, opening new avenues for exploring the plastic behavior of ceramics.

This article is licensed under a Creative Commons Attribution 4.0 International License.

100441

Neural network potential for dislocation plasticity in ceramics

Nature Research

English

2024-11-22

npj Computational Materials

10

266

https://doi.org/10.1038/s41524-024-01456-7

https://hdl.handle.net/11094/100441