| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Refractory Metals 2023
|
| Presentation Title |
Building Fundamentals for Data-Driven Discovery of Refractory High Entropy Alloys with Targeted Mechanical Properties via First-principles and Machine Learning |
| Author(s) |
Shun-Li Shang, Adam M. Krajewski, Arindam Debnath, Shuang Lin, Wesley Reinhart, Zi-Kui Liu |
| On-Site Speaker (Planned) |
Shun-Li Shang |
| Abstract Scope |
Ocean of data is fundamental to creating a sustainable ecosystem to understand, design, and discover materials. Taking refractory high entropy alloys (RHEA) as an example, we first show data generation and correlation analysis for pure elements, dilute alloys, and concentrated alloys based on data available in the literature and our high-throughput simulations by first-principles and CALPHAD modeling guided by machine learning. Second, we show cloud-based data infrastructures, the Material-Property-Descriptor (MPDD) Database and ULtrahigh Temperature Refractory Alloys (ULTERA) Database, used to store and process data on atomic structures and RHEA. Finally, we demonstrate an application of our data to inversely design the RHEA with properties, e.g., superior fracture toughness, excellent creep resistance, and outstanding tensile strength and hardness. This work not only provides fundamental properties of pure elements and alloys but also creates a toolset for data-driven understanding and discovery of materials, as illustrated with the mechanical properties of RHEA. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Computational Materials Science & Engineering, High-Entropy Alloys, High-Temperature Materials |