In this paper, we have developed models based on Deep-Learning Neural Network (DNN) for accurately predicting lattice constants of half-Heusler alloys. A commercial software WIEN2k employing the Density Functional Theory (DFT) is first used to generate data of the lattice constants of 377 half-Heusler alloys for the training, testing and validation of the models. These models use elemental symbols or/and ionic radii as input parameters. The model that uses only symbols of the constituent element and the model that uses symbols in combination with the radii of the ions predict lattice constants of half-Heusler alloys with an average error of less than 1% to the data obtained from the WIEN2k calculations. The average error stays below 2% in the prediction by the model that uses radii of the ions alone. These results show a great promise for these models to be extended for the prediction of structural and elastic properties of not only new half-Heusler alloys but also other new materials such as full-Heusler, spinels, perovskites, etc. with greater convenience, saving hours of computation time.

中文翻译：

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使用元素符号和/或离子半径通过深度神经网络模型预测半赫斯勒合金的晶格常数

在本文中，我们开发了基于深度学习神经网络 (DNN) 的模型，用于准确预测半赫斯勒合金的晶格常数。采用密度泛函理论 (DFT) 的商业软件 WIEN2k 首次用于生成 377 种半赫斯勒合金的晶格常数数据，用于模型的训练、测试和验证。这些模型使用元素符号或/和离子半径作为输入参数。仅使用组成元素符号的模型和使用符号与离子半径相结合的模型预测半赫斯勒合金的晶格常数与从 WIEN2k 计算获得的数据的平均误差小于 1%。仅使用离子半径的模型预测的平均误差保持在 2% 以下。