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In this work, a dataset including structural and mechanical properties of refractory multicomponent alloys was developed by fusing computations of phase diagram (CALPHAD) and density functional theory (DFT). The refractory multicomponent alloys, also named refractory complex concentrated alloys (CCAs) which contain 2–5 types of refractory elements were constructed based on Special Quasi-random Structure (SQS). The phase of alloys was predicted using CALPHAD and the mechanical property of alloys with stable and single body-centered cubic (BCC) at high temperature (over 1,500°C) was investigated using DFT-based simulation. As a result, a dataset with 393 refractory alloys and 12 features, including volume, melting temperature, density, energy, elastic constants, mechanical moduli, and hardness, were produced. To test the capability of the dataset on supporting machine learning (ML) study to investigate the property of CCAs, CALPHAD, and DFT calculations were compared with principal components analysis (PCA) technique and rule of mixture (ROM), respectively. It is demonstrated that the CALPHAD and DFT results are more in line with experimental observations for the alloy phase, structural and mechanical properties. Furthermore, the data were utilized to train a verity of ML models to predict the performance of certain CCAs with advanced mechanical properties, highlighting the usefulness of the dataset for ML technique on CCA property prediction.
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Data-driven inverse design of MoNbTiVWZr refractory multicomponent alloys: Microstructure and mechanical properties
Multicomponent refractory alloys have the potential to operate in high-temperature environments. Alloys with heterogeneous/composite microstructure exhibit an optimal combination of high strength and ductility. The present work generates designed compositions using high-throughput computational and machine-learning (ML) models based on elements Mo-Nb-Ti-V-W-Zr manufactured utilizing vacuum arc melting. The experimentally observed phases were consistent with CALPHAD and Scheil simulations. ML models were used to predict the room temperature mechanical properties of the alloy and were validated with experimental mechanical data obtained from the three-point bending and compression tests. This work collectively showcases a data-driven, inverse design methodology that can effectively identify new promising multicomponent refractory alloys.
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- Award ID(s):
- 1652575
- PAR ID:
- 10583733
- Publisher / Repository:
- Materials Science and Engineering A
- Date Published:
- Journal Name:
- Materials Science and Engineering: A
- Volume:
- 918
- Issue:
- C
- ISSN:
- 0921-5093
- Page Range / eLocation ID:
- 147475
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.msea.2024.147475
