About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
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High Entropy Materials: Concentrated Solid Solutions, Intermetallics, Ceramics, Functional Materials and Beyond III
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Presentation Title |
High-entropy Materials Design by Integrating the First-principles Calculations and Machine Learning: A Case Study in the Al-Co-Cr-Fe-Ni System |
Author(s) |
Guangchen Liu, Songge Yang, Yu Zhong |
On-Site Speaker (Planned) |
Guangchen Liu |
Abstract Scope |
The first-principles calculation is widely used in high entropy materials. However, this approach may consume many computational resources for complex systems, limiting the construction of property maps for the corresponding materials over a full composition range. In this work, the most common Al-Co-Cr-Fe-Ni system (both fcc and bcc) is selected for our investigation. We formulate a materials design strategy that combines first-principles calculation results and machine learning models to establish a robust database of properties (e.g., phase stabilities and elastic constants): starting from unary, binary, ternary, and quaternary, then extending into high-order systems. Moreover, analyzing and screening this database can further inspire discovering and designing new high entropy materials. |