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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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Mechanical properties of pure elements from a comprehensive first-principles study to data-driven insights
Unraveling mechanical properties from fundamentals is far from complete despite their vital role in determining applicability and longevity for a given material. Here, we perform a comprehensive study related to mechanical properties of 60 pure elements in bcc, fcc, hcp, and/or diamond structures by means of pure alias shear and pure tensile deformations via density functional theory (DFT) based calculations alongside a broad review of existing literature. The present data compilation enables a detailed correlation analysis of mechanical properties, focusing on DFT-based ideal shear and tensile strengths (τis and σit), stable and unstable stacking fault energies (γsf and γus), surface energy (γs), and vacancy activation energy (QV); and experimental hardness (HB), ultimate tensile strength (σUT), fracture toughness (KIc), and elongation (εEL). The present work examines models, identifies outliers, and provides insights into mechanical properties, for example, (i) HB is correlated by QV, σUT by γs or γus, and KIc by γs; (ii) data outliers are identified for Cr (related to τis, γs, QV, and σUT), Be (τis, γsf, γus, and QV), Hf (HB and KIc), Yb (all properties), and Pt (γsf vs. γus); and (iii) τis σit, γsf, γus, γs, QV, and HB are highly correlated to elemental attributes, while σUT, KIc, and especially εEL are less correlated due mainly to experimental uncertainty. In particular, the present data compilation provides a solid foundation to model properties such as γs and τis of multicomponent alloys and τis of unstable structures like bcc Ti, Zr, and Hf.
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- PAR ID:
- 10583096
- Publisher / Repository:
- Materials Science & Engineering A
- Date Published:
- Journal Name:
- Materials Science and Engineering: A
- Volume:
- 918
- Issue:
- C
- ISSN:
- 0921-5093
- Page Range / eLocation ID:
- 147446
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.msea.2024.147446
