About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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Materials Genome, CALPHAD, and a Career over the Span of 20, 50, and 60 Years: An FMD/SMD Symposium in Honor of Zi-Kui Liu
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Presentation Title |
A Comprehensive First-principles and Machine Learning Study of Pure Elements and Alloys: From Pure Shear Deformation to Data-driven Insights into Mechanical Properties |
Author(s) |
Shun-Li Shang, Yi Wang, Jingjing Li, Allison M. Beese, Zi-Kui Liu |
On-Site Speaker (Planned) |
Shun-Li Shang |
Abstract Scope |
Advance in machine learning (ML), especially in the cooperation between ML predictions, first-principles calculations, and experimental verification is emerging as a new paradigm to understand fundamentals, verify, analyze, and predict data, and design and discover materials. Here, first we perform high-throughput first-principles calculations for 60 pure elements and 25 dilute and concentrated alloys in the bcc, fcc, and hcp lattices, resulting in fundamental properties of ideal shear strength, stable and unstable stacking fault energies. Then, ML-based correlation analyses are performed to understand these fundamentals with respect to elemental attributes; and these fundamentals make it possible for data-driven insights into mechanical properties of pure elements and alloys, including hardness, fracture toughness, tensile strength, and elongation. The present study provides not only fundamental properties of pure elements and alloys, including high-entropy alloys, but also methodology regarding data-driven understanding and predictions as illustrated with mechanical properties. |
Proceedings Inclusion? |
Planned: |
Keywords |
Mechanical Properties, Computational Materials Science & Engineering, Machine Learning |