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ABSTRACTThis review systematically analyzes over 200 publications to explore the growing role of data-driven methods and their potential benefits in accelerating alloy development. The review presents a comprehensive overview of different aspects of alloy innovation by machine learning and other computational approaches used in recent years. These methods harness the power of advanced simulation techniques and data analytics to expedite materials’ discovery, predict properties, and optimize performance. Through analysis, significant trends and disparities within the data discerned, while highlighting previously overlooked research gaps, thus underscoring areas that require further exploration. Machine Learning techniques are widely applied across various alloys, with a pronounced emphasis on steel and High Entropy Alloys. Notably, researchers primarily investigate the physical, mechanical, and catalytic properties of materials. In terms of methodology, while 68% of the examined papers rely on a single machine learning model, the remainder employ a range of 2 to 12 models, with Neural Network being the most prevalent choice. However, a notable concern arises as 53% of these papers do not share their dataset, and a staggering 81% do not provide access to their code. Paramount importance of adopting a systematic approach when scrutinizing machine learning methodologies is underscored. Analysis shows lack of consistency and diversity in the methods employed by researchers in the field of alloy development, highlighting the potential for improvement through standardization. The critical analysis of the literature not only reveals prevailing trends and patterns but also shines a light on the inherent limitations within the traditional trial-and-error paradigm.

θ-Al13Fe4 particles form as a primary Fe intermetallic compound (Fe-IMC) during the casting of commercial Al metals and alloys that inevitably contain Fe and Si as impurities. Moreover, the excellent mechanical performances of the Al-Cu alloys demand knowledge about the structural chemistry of the Fe-IMCs, including the θ-phase in the quaternary Al-Cu-Fe-Si system. Here, we investigate the stability, crystal structure, and electronic and mechanical properties of the Cu and Si co-doped θ-phase using a first-principles density-functional theory approach. The calculations reveal high stability of a quaternary θ-phase with chemical composition (Al0.8718Cu0.0256Si0.1026)13Fe4 at ambient conditions. Thermodynamics and statistical analysis show a broad range of Si content in the structure at the casting temperature. The Cu and Si (co-)doping enhances the bulk modulus of the compounds. The calculated bulk modulus of the quaternary θ-phase is 129 GPa. The findings help characterize the θ-phase in the quaternary Al-Si-Fe-Cu system and understand the formation of the θ-phase and related phase transformations in the various Al alloys during casting.

In this work, we investigate Cu solution in θ-Al13Fe4 and related Al-rich ω-Al7Cu2Fe and Al2Cu phases in the Al-Cu-Fe system using the first-principles density functional theory (DFT) with on-site Coulomb interaction correction. The results show preference of Cu at Al7, forming a ternary θ-Al76Cu2Fe24 at ambient conditions, and both Al7 and Al9 sites (in Grin’s note), forming θ-(Al76−xCu2+x)Fe24 at a high temperature. The relative stability of the Al-rich compounds and their crystal and electronic properties are investigated. We show the importance of the Hubbard U correction to the standard DFT functionals for Cu-containing metallic materials. This study helps characterize the intermetallic compounds in Cu-containing Al alloys, and helps further control Fe-containing intermetallic compounds in the solidification of Al-based alloys.

Abstract The effect of mixed partial occupation of metal sites on the phase stability of the γ-Cr23−x Fe x C6 (x = 0–3) carbides is explored as function of composition and temperature. Ab initio calculations combined with statistical thermodynamics approaches reveal that the site occupation of the carbides may be incorrectly predicted when only the commonly used approach of full sublattice occupation is considered. We found that the γ-M23C6 structure can be understood as a familiar sodium chloride structure with positively charged rhombic dodecahedron (M(4a) M12 (48h)) and negatively charged cubo-octahedron (M8 (32f) C6 (24e)) super-ion clusters, together with interstitial metal atoms at the 8c sites. The stability of the partially occupied phase can be easily rationalized on the basis of a super-ion analysis of the carbide phase. This new understanding of γ-M23C6 carbides may facilitate further development of high-chromium heat-resistant steels.