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
IF 8.3 1区 材料科学Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Jie Zhang , Yi Kong , Li Chen , Nikola Koutná , Paul H. Mayrhofer
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引用次数: 0
Abstract
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.