Jie Zhang , Yi Kong , Li Chen , Nikola Koutná , Paul H. Mayrhofer
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引用次数: 0
摘要
开发过渡金属合金化 (Ti,Al)N 薄膜已成为实现优化机械和热性能的常用策略。然而,选择合适的合金元素应考虑对铝溶解度的影响,因为铝溶解度直接影响沉积过程中的相稳定性。在此,我们使用高通量形成焓计算来评估 TM 合金 (Ti,Al,TM)N的立方 (c) 相与六方钨锆石型 (w-) 相的稳定性。这个成分有限的数据集可用于拟合多个机器学习(ML)模型,从而预测整个成分范围内的相稳定性。在所有模型中,使用经遗传算法预处理的特征描述器进行线性回归的准确度最高。对于 Ta、Nb、Mo 和 W 的添加量低于 ∼10 at.%,我们的 ML 模型预测由于 Al 的溶解度增加,c-(Ti,Al,TM)N 的稳定性会增强。其他合金元素,尤其是来自 IIIB 族的 Sc 和 Y 以及来自 IVB 族的 Hf 和 Zr 会降低立方体的可迁移溶解度极限。与现有的实验数据一致,除 Cr 和 V 外,所有过渡金属都会增加 c-(Ti,Al,TM)N 和 w-(Ti,Al,TM)N 的体积。
Predicting the formation enthalpy and phase stability of (Ti,Al,TM)N (TM = III-VIB group transition metals) by high-throughput ab initio calculations and machine learning
The development of transition-metal-alloyed (Ti,Al)N thin films has become a common strategy to achieve optimized mechanical and thermal properties. Selection of a suitable alloying element, however, should consider the effect on Al solubility, directly influencing phase stability during the deposition. Here we use high-throughput ab initio formation enthalpy calculations to assess stability of the cubic (c) vs. hexagonal wurtzite-type (w-) phase of TM-alloyed (Ti,Al,TM)N. This compositionally-limited ab initio dataset serves to fit several machine-learning (ML) models enabling phase stability predictions over the entire compositional range. Of all the models, the linear regression using Magpie feature descriptor pre-processed by a genetic algorithm has the highest accuracy. For Ta, Nb, Mo, and W addition below ∼10 at.%, our ML model predicts enhanced stability of c-(Ti,Al,TM)N due to increased solubility of Al. Other alloying elements, especially Sc and Y from IIIB group and Hf and Zr from IVB group, decrease the cubic metastable solubility limit. In agreement with available experimental data, all transition metals except for Cr and V increase the volume of c-(Ti,Al,TM)N and w-(Ti,Al,TM)N.
期刊介绍:
Acta Materialia serves as a platform for publishing full-length, original papers and commissioned overviews that contribute to a profound understanding of the correlation between the processing, structure, and properties of inorganic materials. The journal seeks papers with high impact potential or those that significantly propel the field forward. The scope includes the atomic and molecular arrangements, chemical and electronic structures, and microstructure of materials, focusing on their mechanical or functional behavior across all length scales, including nanostructures.