论文标题
部分可观测时空混沌系统的无模型预测
Efficient atomistic simulations of radiation damage in W and W-Mo using machine-learning potentials
论文作者
论文摘要
高斯近似电势（GAP）是一种准确的机器学习间的原子势，最近扩展到包括辐射效应的描述。在这项研究中，我们试图通过使用经典的分子动力学对50-50 w-MO合金中的原发性辐射损伤进行建模，以验证更快的间隙（称为列表GAP（TABGAP））。我们发现W-MO表现出与纯W中的持续缺陷相似的数量。我们还观察到W-MO具有在级联反应初始阶段产生的缺陷的更有效的重组，在某些情况下，与纯W不同，在级联反应后所有防御的重新组合。此外，我们观察到TabGap比GAP快两个数量级，但产生了可比数量的尚存的缺陷和集群大小。在结合成簇的间隙的比例中注意到了很小的差异。
The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential that was recently extended to include the description of radiation effects. In this study, we seek to validate a faster version of GAP, known as tabulated GAP (tabGAP), by modelling primary radiation damage in 50-50 W-Mo alloys and pure W using classical molecular dynamics. We find that W-Mo exhibits a similar number of surviving defects as in pure W. We also observe W-Mo to possess both more efficient recombination of defects produced during the initial phase of the cascades, and in some cases, unlike pure W, recombination of all defects after the cascades cooled down. Furthermore, we observe that the tabGAP is two orders of magnitude faster than GAP, but produces a comparable number of surviving defects and cluster sizes. A small difference is noted in the fraction of interstitials that are bound into clusters.
