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arXiv 2405.03344cond-mat.mtrl-sci
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钨在连续碰撞级联中缺陷演化的分子动力学模拟

Molecular dynamics simulations of the defect evolution in tungsten on successive collision cascades

Utkarsh Bhardwaj, Manoj Warrier

AI总结:

本研究使用EAM和机器学习势SNAP模拟钨中连续碰撞级联,发现SNAP势在肿胀和表面缺陷上更符合实验,但预测了大量小固着缺陷。

摘要:

在钨中使用两种不同的原子间势(IAP)对同一模拟域内的连续碰撞级联进行了分子动力学(MD)模拟,其中一种基于EAM,另一种是“量子精确”的机器学习势SNAP。针对20 keV和50 keV的初级离位原子(PKA)能量,将微观结构变化作为每原子离位次数(dpa)的函数进行分析,辐照剂量分别达到0.1和0.2 dpa。每种情况进行了五次模拟以观察损伤演化中的随机差异。进行了详细的缺陷分析,以观察表面缺陷数量、缺陷密度、缺陷形态和尺寸分布等不同参数随dpa的变化。我们探讨了依赖于所用IAP和依赖于PKA能量的属性,并指出了它们与不同dpa值下实验结果的相似性。SNAP势在肿胀和表面缺陷数量上与实验更为吻合,但也预测了大量小固着缺陷的存在,这可能对微观结构演化和材料性能产生明确影响。

英文摘要:

Molecular dynamics (MD) simulations of successive collision cascades within the same simulation domain were performed using two different inter-atomic potentials (IAP) in tungsten, one EAM based and the other a `quantum accurate' machine learning potential, SNAP. The micro-structural changes are analyzed as a function of displacements per atom (dpa) for primary knock-on atom (PKA) energies of 20 keV and 50 keV, reaching up-to irradiation dose of 0.1 and 0.2 dpa, respectively. Five sample simulations are carried out for each case for observing stochastic differences in the evolution of damage. A detailed defect analysis is carried out to observe changes in different parameters such as the number of surface defects, defect density, defect morphology and size distribution etc., as a function of dpa. We explore the properties that are sensitive to the IAP used and those that are sensitive to the PKA energy and note their similarities with experimental results at various dpa values. The SNAP potential shows better agreement with the experiments for swelling and number of surface defects. However, it also predicts presence of high number of small sessile defects which may have definite affect on the processes of microstructural evolution and material properties.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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