Mo-Re-W Alloys for High temperature Applications: Phase stability, elasticity, and thermal property insights via multi-cell Monte Carlo and machine learning
Document Type
Article
Publication Date
8-9-2024
Abstract
The increasing demand for materials capable of withstanding high temperatures and harsh environments necessitates the discovery of advanced alloys. This study introduces a computational routine to predict solid-state phase stability and calculates elastic constants to determine high temperature viability. With it, machine learning models were trained on 1,014 Mo-Re-W structures to enable a large compilation of elastic and thermal properties over the complete Mo-Re-W compositional domain with extreme resolution. A series of heat maps spanning the full compositional domain were generated to visually present the impact of alloy constituents on the alloy properties. Our findings identified a balanced (Mo,W) + Re blend as a promising composition for high temperature applications, attributed to a strong and stable (Mo,W) matrix with high Re content and the formation of strengthening (W,Re) precipitates that enhanced mechanical performance at 1600°C. Several Mo-Re-W compositions were manufactured to experimentally validate the computational predictions. This approach provides an efficient and system-agnostic pathway for designing and optimizing alloys for high-temperature applications.
Source Publication
Materials & Design
Recommended Citation
Doležal, T. D., Valverde, N. A., Yuwono, J. A., & Kemnitz, R. A. (2024). Mo-Re-W alloys for high temperature applications: Phase stability, elasticity, and thermal property insights via multi-cell Monte Carlo and machine learning. Materials & Design, 244, 113226. https://doi.org/10.1016/j.matdes.2024.113226
Graphical abstract for Mo-Re-W article by Dolezal et al
Comments
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This is an Open Access article published by Elsevier and distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. CC BY-NC-ND 4.0
Funding notes: This work was fully supported by computational resource allocations provided by the Department of Defense high performance computing through AFRL's HPC Mustang. This research was supported by the Air Force Research Laboratory's Materials and Manufacturing Directorate.
Data availability: The (MC)2 code used for this work is available through a link on the article page, via the DOI or Link on this page.