Tensor equation of state for copper and aluminum

IF 3.1 3区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Boris A. Panchenko , Eugenii V. Fomin , Alexander E. Mayer
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Abstract

Increasing the strain rate up to about 1/ns in the conditions of ultra-short-pulse powerful laser irradiation of thin metal foils of submicron thickness reveals very strong elastic precursors, for which decoupling of pressure and stress deviators is unreasonable. A nonlinear stress–strain relationship (tensor equation of state–TEOS) is required for establishing theoretical models of such dynamic processes and unsteady shock waves. We proposed an ANN-TEOS model, which adopts an artificial neural network (ANN) trained on the data of density functional theory (DFT) calculations for the cold curve and analytical form for thermal contributions fitted to molecular dynamics (MD) data. We showed the efficiency of feed-forward ANN in approximation of the cold curve. Besides, the range of applicability of Hooke’s law for approximation of the cold curve was examined. The DFT data were used to train a cold-curve ANN and to fit elastic constants of the Hooke’s law with nonlinear corrections for copper and aluminum. Whereas the ANN is applicable for a complex approximation within a wide range of deformed states, the Hooke’s law applicability is restricted to the strain level of about 0.1. MD simulations were used to construct and fit the thermal contributions. The developed ANN-TEOS model was applied to calculate the shock adiabats for both plastic shock wave (nearly hydrostatic omnidirectional loading) and elastic shock wave (uniaxial loading). Elastic shock Hugoniots lie above the plastic ones for both metals providing an opportunity for a shock wave to split into elastic precursor and plastic wave even for conditions, in which the plastic shock wave velocity exceeds the longitudinal sound speed. A simultaneous consideration of TEOS and plasticity model is required for the prediction of splitting and two-wave structure. Our comparison of several interatomic potentials with the stress sates calculated by means of DFT showed much higher precision of the classical force field in comparison with the examined machine-learning potentials for the considered problem of severe deformation. This result elucidates one more time the known problem of the restricted range of applicability of the machine-learning potentials and the need to include the required range of states in the training dataset for these potentials.

Abstract Image

铜和铝的张量状态方程
在超短脉冲强激光照射下,将亚微米厚度金属薄片的应变速率提高到1/ns左右,显示出非常强的弹性前驱体,压力和应力偏差的解耦是不合理的。建立这种动态过程和非定常激波的理论模型需要非线性应力-应变关系(状态张量方程)。本文提出了一种基于密度泛函理论(DFT)计算数据训练的人工神经网络(ANN)的ANN- teos模型,用于拟合分子动力学(MD)数据的冷曲线和热贡献的解析形式。我们证明了前馈神经网络在逼近冷曲线方面的有效性。此外,还考察了胡克定律在冷曲线近似中的适用范围。利用DFT数据训练冷曲线人工神经网络,并对铜和铝的胡克定律弹性常数进行非线性修正拟合。尽管人工神经网络适用于大范围变形状态下的复杂近似,但胡克定律的适用性仅限于0.1左右的应变水平。MD模拟用于构造和拟合热贡献。将所建立的ANN-TEOS模型应用于塑性激波(近静水全向加载)和弹性激波(单轴加载)的冲击绝热计算。弹性激波在两种金属的塑性激波之上,即使在塑性激波速度超过纵速的情况下,也为激波分裂成弹性前体和塑性波提供了机会。劈裂和双波结构的预测需要同时考虑TEOS和塑性模型。我们将几个原子间势与DFT计算的应力状态进行了比较,结果表明,对于所考虑的严重变形问题,经典力场的精度要比机器学习势高得多。这一结果再次阐明了机器学习潜力的适用范围有限的已知问题,以及需要在这些潜力的训练数据集中包括所需的状态范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computational Materials Science
Computational Materials Science 工程技术-材料科学:综合
CiteScore
6.50
自引率
6.10%
发文量
665
审稿时长
26 days
期刊介绍: The goal of Computational Materials Science is to report on results that provide new or unique insights into, or significantly expand our understanding of, the properties of materials or phenomena associated with their design, synthesis, processing, characterization, and utilization. To be relevant to the journal, the results should be applied or applicable to specific material systems that are discussed within the submission.
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