Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Fei Shuang, Kai Liu, Yucheng Ji, Wei Gao, Luca Laurenti, Poulumi Dey
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引用次数: 0

Abstract

Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms. However, existing machine learning interatomic potentials (MLIPs) often fall short in adequately describing these defects, as their large characteristic scales exceed the computational limits of first-principles calculations. To address this challenge, we present a computational framework combining a defect genome constructed via empirical interatomic potential-guided sampling, with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations. The effectiveness of this approach was validated through simulations of nanoindentation, tensile deformation, and fracture in BCC tungsten. This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically.

Abstract Image

通过经典的电位引导采样和自动结构重建来模拟金属中的广泛缺陷
位错网络和一般晶界等扩展缺陷在金属中普遍存在,准确模拟这些广泛缺陷对于阐明其变形机制至关重要。然而,现有的机器学习原子间势(mlip)往往不能充分描述这些缺陷,因为它们的大特征尺度超过了第一性原理计算的计算极限。为了解决这一挑战,我们提出了一个计算框架,结合了通过经验原子间电位引导采样构建的缺陷基因组,以及通过将原子簇转换为周期性配置实现一般缺陷精确第一性原理建模的自动重建技术。通过模拟BCC钨的纳米压痕、拉伸变形和断裂,验证了该方法的有效性。该框架提高了晶体材料中扩展缺陷的建模准确性,并通过战略性地利用缺陷基因组为推进MLIP的发展提供了坚实的基础。
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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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