Deep-Learning Potential Molecular Dynamics Study on Nanopolycrystalline Al–Er Alloys: Effects of Er Concentration, Grain Boundary Segregation, and Grain Size on Plastic Deformation

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Zhen Chang, Li Feng, Hong-Tao Xue*, Yan-Hong Yang*, Jun-Qiang Ren, Fu-Ling Tang, Xue-Feng Lu and Jun-Chen Li, 
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Abstract

Understanding the tensile mechanical properties of Al–Er alloys at the atomic scale is essential, and molecular dynamics (MD) simulations offer valuable insights. However, these simulations are constrained by the unavailability of suitable interatomic potentials. In this study, the deep potential (DP) approach, aided by high-throughput first-principles calculations, was utilized to develop an Al–Er interatomic potential specifically for MD simulations. Systematic comparisons between the physical properties (e.g., energy-volume curves, melting point, elastic constants) predicted by the DP model and those obtained from density functional theory (DFT) demonstrated that the developed DP model for Al–Er alloys possesses reliable predictive capabilities while retaining DFT-level accuracy. Our findings confirm that Al3Er, Al2Er, and AlEr2 exhibit mechanical stability. The calculated melting point of Al3Er (1398 K) shows a 57 K deviation from the experimental value (1341 K). With the Er content increasing from 0.01% to 0.064 at.% in Al–Er alloys, the grain boundary (GB) concentration of Er atoms increases from 0.03 to 0.07% following Monte Carlo (MC) annealing optimization. The Al-0.05 at.%Er alloy exhibits the highest yield strength, with an increase of 0.128 GPa (6.1%) compared to pure Al. For Al-0.05 at.%Er alloys with varying average grain sizes, the GB concentration of Er atoms increases by about 1.4–1.6 times after MC annealing compared to the average Er content. Additionally, the Al–Er alloys reach the peak yield strength of 2.214 GPa when the average grain size is 11.72 nm. The GB segregation of Er atoms lowers the system energy and thus enhances stability. Notable changes in the segregation behavior of Er atoms were observed with increasing Er concentration and decreasing grain size. These results would facilitate the understanding of the mechanical characteristics of Al–Er alloys and offer a theoretical basis for developing advanced nanopolycrystalline Al–Er alloys.

Abstract Image

纳米多晶Al-Er合金的深度学习势分子动力学研究:Er浓度、晶界偏析和晶粒尺寸对塑性变形的影响
在原子尺度上理解Al-Er合金的拉伸力学性能是必不可少的,分子动力学(MD)模拟提供了有价值的见解。然而,这些模拟受到无法获得合适的原子间势的限制。在本研究中,利用深电位(DP)方法,在高通量第一性原理计算的辅助下,开发了专门用于MD模拟的Al-Er原子间电位。通过与密度泛函理论(DFT)的物理性质(如能量体积曲线、熔点、弹性常数)预测结果的系统比较表明,所建立的Al-Er合金DP模型在保持DFT级精度的同时具有可靠的预测能力。我们的发现证实了Al3Er、Al2Er和AlEr2具有机械稳定性。随着Er含量从0.01%增加到0.064 at, Al3Er的熔点(1398 K)与实验值(1341 K)相差57 K。在Al-Er合金中,经蒙特卡罗(MC)退火优化后,Er原子的晶界(GB)浓度由0.03%提高到0.07%。Al-0.05 at。%Er合金的屈服强度最高,比纯Al合金的屈服强度提高了0.128 GPa(6.1%)。在不同平均晶粒尺寸的%Er合金中,与平均Er含量相比,MC退火后的Er原子的GB浓度增加了约1.4 ~ 1.6倍。当平均晶粒尺寸为11.72 nm时,Al-Er合金的屈服强度达到2.214 GPa。铒原子的GB偏析降低了体系能量,从而提高了稳定性。随着铒浓度的增加和晶粒尺寸的减小,铒原子的偏析行为发生了显著的变化。这些结果有助于进一步了解Al-Er合金的力学特性,为开发先进的纳米多晶Al-Er合金提供理论依据。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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