用过渡态团簇展开法预测空位介导扩散激活能
Predicting activation energies for vacancy-mediated diffusion in alloys using a transition-state cluster expansion
摘要：
由第一性原理计算参数化的动力学蒙特卡罗模型被广泛用于模拟原子扩散。然而，由于扩散原子周围可能存在各种各样的局部环境，准确预测置换合金中扩散的激活能仍然具有挑战性。我们解决这一挑战，使用一个集群扩展模型，明确包括一个子晶格的网站代表过渡态和评估其准确性与其他模型相比，如断键模型和马库斯理论相关的模型，通过模拟空位介导的扩散在铂镍纳米粒子。我们发现，对于小的训练集，集群扩展的预测误差与其他模型的预测误差相似，但是对于较大的训练集，集群扩展的预测误差显著低于执行速度相当的其他模型。在较简单的模型中，与Marcus理论相关的模型产生了与簇膨胀最相似的纳米颗粒演化的预测，并且两种方法的加权平均值在所有训练集大小中对活化能的预测误差最低。
英文摘要：
Kinetic Monte Carlo models parameterized by first principles calculations are widely used to simulate atomic diffusion. However, accurately predicting the activation energies for diffusion in substitutional alloys remains challenging due to the wide variety of local environments that may exist around the diffusing atom. We address this challenge using a cluster expansion model that explicitly includes a sublattice of sites representing transition states and assess its accuracy in comparison with other models, such as the broken bond model and a model related to Marcus theory, by modeling vacancy-mediated diffusion in Pt-Ni nanoparticles. We find that the prediction error of the cluster expansion is similar to that of other models for small training sets, but with larger training sets the cluster expansion has a significantly lower prediction error than the other models with comparable execution speed. Of the simpler models, the model related to Marcus theory yields predictions of nanoparticle evolution that are most similar to those of the cluster expansion, and a weighted average of the two approaches has the lowest prediction error for activation energies across all training set sizes.
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