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arXiv 2402.00359cond-mat.mtrl-sci
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基于五种原子间势的钨碰撞级联初级损伤中缺陷团簇形貌的统计研究

Statistical Study of the Defect Cluster Morphology in the Primary Damage of Tungsten from Collision Cascades from Five Inter-atomic potentials

M. Warrier, U. Bhardwaj

AI总结:

本研究采用五种原子间势开展3500次钨碰撞级联分子动力学模拟,统计分析缺陷团簇特征,明确了自间隙原子形成能差异对形貌的关键作用,及多参数耦合对缺陷产生的显著影响。

摘要:

初级损伤过程中形成的缺陷团簇的尺寸和形貌对辐照材料后续的微观结构演化至关重要。本研究采用五种原子间势(IAP)对钨(W)中的碰撞级联开展了分子动力学(MD)模拟,这五种势分别是:具备量子精度的机器学习谱邻域分析势(W-SNAP)、基于机器学习的tabGAP势,以及三种嵌入原子法(EAM)势。研究共开展了3500次MD模拟,初级撞出原子(PKA)的能量设置为5、10、20、50、75、100和150 keV。为保证统计有效性,每种能量下均沿100个随机方向发射PKA。研究借助面向大规模碰撞级联数据库的网页工具CSaransh开展分析,量化了以下内容:(i)缺陷数量(孤立缺陷与团簇缺陷)、(ii)缺陷团簇形貌、(iii)缺陷团簇尺寸分布,以及(iv)形成的子级联数量。研究表明,<1 1 0>与<1 1 1>方向上自间隙原子哑铃形结构的形成能差异,对缺陷团簇形貌具有关键影响。结果显示,IAP的刚度和作用范围单独来看并不会影响缺陷数量,但这些参数与缺陷形成能、位移阈值能量及其他因素相结合时,会对缺陷产生过程造成显著影响。

英文摘要:

The size and morphology of defect clusters formed during primary damage play a crucial role in the subsequent microstructural evolution of irradiated materials. Molecular dynamics (MD) simulations of collision cascades in tungsten (W) were performed using five interatomic potentials (IAPs): the quantum-accurate machine-learned Spectral Neighbor Analysis Potential (W-SNAP), the machine learning-based tabGAP potential, and three embedded-atom method (EAM) potentials. A total of 3,500 MD simulations were conducted with primary knock-on atoms (PKAs) at energies of 5, 10, 20, 50, 75, 100, and 150 keV. PKAs were launched in 100 random directions at each energy to ensure statistical validity. Analysis was performed using CSaransh , a web-based tool for large-scale collision cascade databases, to quantify: (i) the number of defects (isolated and clustered), (ii) defect cluster morphologies, (iii) defect cluster size distributions and (iv) the number of sub-cascades formed. We show that the difference in the formation energy of self interstitial atom dumbells along the <1 1 0> and <1 1 1> directions critically influence defect cluster morphology. Our results indicate that IAP stiffness and interaction range independently do not affect defect count. However, these parameters combined with defect formation energies, threshold displacement energies, and other factors significantly influence defect production.

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