Abstract Diffusion coefficient play a crucial role in material designing, and physical phenomenon explaining during the material preparation and post-treatment. However, it is unavailable in some metallic systems. In this paper, based on basic physical properties (including atom properties, lattice parameters, melting temperature, elastic stiffness constant and etc.), the diffusion activate energy model were developed by machine-learning methods. First, the melting temperature (Tm) and elastic stiffness constant (Cij) models were built by machine-learning methods to fill the absent values in properties. Second, the diffusion activate energy (Q) model was built, and a hybrid features selection method was used to decrease features from 73 to 11 in the model. The Tm, Cij and Q models showed a good predictive ability and goodness of fit. Finally, features in the models were analyzed and compared with the parameters in various prior models. This work provides further understanding on the mechanism of the melting process, elastic deformation and diffusion process. Moreover, the models could be able to provide an easy and reliable method to obtain the diffusion coefficients in bcc, fcc, and hcp alloys when they are needed but unavailable.

中文翻译：

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通过机器学习方法预测 fcc、bcc 和 hcp 相中的扩散系数保持稳定或亚稳定

摘要 扩散系数在材料设计中起着至关重要的作用，是解释材料制备和后处理过程中的物理现象。但是，它在某些金属系统中不可用。本文基于基本物理性质（包括原子性质、晶格参数、熔化温度、弹性刚度常数等），通过机器学习方法建立了扩散激活能模型。首先，熔化温度 (Tm) 和弹性刚度常数 (Cij) 模型是通过机器学习方法建立的，以填补属性中的缺失值。其次，建立扩散激活能（Q）模型，并采用混合特征选择方法将模型中的特征从73个减少到11个。Tm、Cij 和 Q 模型显示出良好的预测能力和拟合优度。最后，对模型中的特征进行了分析，并与各种先前模型中的参数进行了比较。这项工作提供了对熔化过程、弹性变形和扩散过程的机制的进一步理解。此外，这些模型可以提供一种简单可靠的方法来在需要但不可用时获得 bcc、fcc 和 hcp 合金的扩散系数。