About this Abstract |
Meeting |
2026 TMS Annual Meeting & Exhibition
|
Symposium
|
Thermodynamics and Kinetics of Alloys IV
|
Presentation Title |
Mapping the Thermodynamic Energy Landscape of High Entropy Materials through Specialized Database and Predictive Model |
Author(s) |
Lin Wang, Bin Ouyang |
On-Site Speaker (Planned) |
Bin Ouyang |
Abstract Scope |
High-entropy materials (HEMs) have garnered growing interest beyond structural applications, expanding into emerging fields such as energy storage and conversion. With the accelerating integration of artificial intelligence in materials research, there is an urgent need for comprehensive, high-quality databases and predictive models tailored to these complex systems. In this talk, we will present our recent efforts in constructing a specialized database containing over 200,000 DFT-calculated compositions of high-entropy alloys (HEAs) and high-entropy metal oxides (HEMOs). We will further demonstrate our theoretical efforts and specialized machine learning model for efficiently explore the thermodynamic energy landscapes of these materials, adaptable to 30 typical metals used for typical alloys and oxides. Lastly, we will also demonstrate some overlooked thermodynamic principles in HEMs. |
Proceedings Inclusion? |
Planned: |
Keywords |
High-Entropy Alloys, Energy Conversion and Storage, Modeling and Simulation |