| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
High Entropy Alloys IX: Structures and Modeling
|
| Presentation Title |
Optimalizing Properties of High Entropy Alloy by Machine Learning and Multiscale Simulations |
| Author(s) |
Jia Li, Yang Chen, Qihong Fang |
| On-Site Speaker (Planned) |
Jia Li |
| Abstract Scope |
High-entropy alloys have attracted wide attention due to their excellent properties, which are expected to be used in nuclear energy, aerospace and other important fields and major equipment. Based on the experimental results combined with high-throughput computing and machine learning, the relationship of “composition-structure-property” is established to guide the development and design of new materials, which overcomes the problems of the low efficiency and waste of resources in the traditional "trial and error method". Therefore, a method based on the high-throughput simulations, theoretical model, and machine learning is adopted to obtain high-strength and low-cost medium-entropy alloy. This method can not only obtain a large number of data quickly and accurately, but also help determine the relationship between the compositions and mechanical properties of medium-entropy alloys. The results show that the combination of the high-throughput simulations, theoretical model, and machine learning is of great significance for the development of alloys with expected properties. The present research will give a foundation for the development of high-throughput experimental and theoretical calculation methods to prepare multicomponent alloys with specific properties. |
| Proceedings Inclusion? |
Planned: TMS Journal: Metallurgical and Materials Transactions |
| Keywords |
Joining, Mechanical Properties, High-Temperature Materials |