Neural network potential for dislocation plasticity in ceramics

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-11-22 DOI:10.1038/s41524-024-01456-7
Shihao Zhang, Yan Li, Shuntaro Suzuki, Atsutomo Nakamura, Shigenobu Ogata
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Abstract

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.

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陶瓷中位错塑性的神经网络潜力
陶瓷中的位错因其巨大的应用潜力而被越来越多的人所认识,如增强固有脆性陶瓷的韧性和定制功能特性。然而,由于陶瓷特有的复杂原子间相互作用,包括离子键和共价键的混合,以及复杂晶体结构中高度扭曲和广泛的位错核心结构,对陶瓷中位错塑性的原子模拟仍具有挑战性。这些复杂性超出了经验原子间位势的能力。因此,构建神经网络电位(NNPs)成为最佳解决方案。然而,由于陶瓷中位错核心构型的复杂性,以及密度泛函理论对包含位错核心的大型原子模型的计算要求,创建包含位错结构的训练数据集十分困难。在这项工作中,我们提出了一种通过高通量计算更容易计算的属性训练数据集。利用这个数据集,我们成功开发了陶瓷中位错塑性的 NNPs,特别是针对三种典型的功能陶瓷:ZnO、GaN 和 SrTiO3。这些 NNPs 有效地捕捉到了差排的非均匀性和带电核心结构和滑垒,以及带电差排之间的长程静电相互作用。通过测量从大规模模拟中得出的快照的相似性和不确定性,以及对各种特性的广泛验证,进一步验证了该数据集的有效性。利用构建的 NNPs,我们通过纳米柱压缩和纳米压痕测试了陶瓷中的位错塑性,结果与实验观察结果非常吻合。这项研究为构建 NNPs 提供了一个有效的框架,可对位错塑性进行详细的原子建模,为探索陶瓷的塑性行为开辟了新的途径。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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