论文标题
使用过渡状态群集扩展预测合金中空置介导的扩散的活化能
Predicting activation energies for vacancy-mediated diffusion in alloys using a transition-state cluster expansion
论文作者
论文摘要
通过第一原理计算参数化的动力学蒙特卡洛模型被广泛用于模拟原子扩散。但是，由于可能存在于扩散原子周围存在的各种局部环境，因此准确地预测替代合金扩散的激活能仍然具有挑战性。我们使用群集扩展模型来应对这一挑战，该模型明确地包括代表过渡状态的地点的司库，并通过对PT-NI纳米颗粒中的空位介导的扩散进行建模，评估其与其他模型相比，例如破碎的键模型和与Marcus理论相关的模型。我们发现，群集扩展的预测误差与小型训练集的其他模型相似，但是在较大的训练集的情况下，群集扩展的预测误差明显低于其他具有可比执行速度的模型。在更简单的模型中，与MARCUS理论相关的模型产生的纳米颗粒演化的预测与集群扩展最相似，并且两种方法的加权平均值具有最低的预测误差，用于所有训练集尺寸的激活能量。
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.
