Machine Learning Based On-The-Fly Kinetic Monte Carlo Simulations of Sluggish Diffusion in Ni-Fe Concentrated Alloys
Machine Learning Based On-The-Fly Kinetic Monte Carlo Simulations of Sluggish Diffusion in Ni-Fe Concentrated Alloys
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DOI：
10.1016/j.jallcom.2022.168457
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发表时间：
2022-12
影响因子：
6.2
通讯作者：
Wenjiang Huang;X. Bai
中科院分区：
文献类型：
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作者：
Wenjiang Huang;X. Bai
高浓度合金的缺陷扩散对其独特的力学和物理性能起着关键作用。在这种合金中，缺陷的扩散取决于其复杂的局部原子环境，并且由于化学无序性而因位置而异。使用标准的微推弹性带（NEB）方法实时确定每个位置的缺陷迁移屏障在计算上是昂贵的，并且通常是不切实际的。在这项工作中，我们将机器学习和动力学蒙特卡罗（KMC）结合起来研究了高浓度Ni-Fe模型合金中空位介导的缓慢扩散。基于预计算的约32,000个NEB势垒，建立了一种基于人工神经网络（ANN）的机器学习模型，以准确预测任意局部原子环境下的空位迁移势垒，包括随机溶液配置和具有短程有序的合金。然后将人工神经网络模型与KMC （ANN-KMC）相结合，实时确定空位迁移势垒，实现了在大温度范围内全成分范围内空位扩散的有效研究。此外，还建立了一个成分和温度相关的跳变尝试频率模型。校正后，ANN-KMC模型预测的空位扩散率与独立分子动力学（MD）和温度加速动力学（TAD）模拟的空位扩散率几乎相同。基于ANN-KMC结果，讨论了该合金体系在高温和低温下的缓慢扩散机制。
Defect diffusion in concentrated alloys plays a key role on governing their unique mechanical and physical properties. In such alloys, defect diffusion depends on its complex local atomic environment and varies from site to site due to the chemical disorder. On-the-fly determination of the defect migration barrier at every site using the standard nudged elastic band (NEB) method is computationally expensive and often impractical. In this work, we couple machine learning and kinetic Monte Carlo (KMC) to study vacancy-mediated sluggish diffusion in concentrated Ni-Fe model alloys. Based on about 32,000 pre-calculated NEB barriers, an artificial neural network (ANN) based machine learning model is developed to accurately predict the vacancy migration barriers for arbitrary local atomic environments, including both random solution configurations and alloys with short-range orders. The ANN model is then coupled with KMC (ANN-KMC) to determine the vacancy migration barriers on-the-fly, enabling an efficient study of the vacancy diffusion in the full composition range at a wide range of temperatures. In addition, a composition and temperature dependent jump attempt frequency model is developed. Upon calibration, the ANN-KMC modeling can predict nearly identical vacancy diffusivities as those obtained from independent molecular dynamics (MD) and temperature accelerated dynamics (TAD) simulations at their accessible temperatures. The sluggish diffusion mechanisms in this specific alloy system at both high and low temperatures are discussed based on the ANN-KMC results.