| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
High Entropy Materials: Concentrated Solid Solution, Intermetallics, Ceramics, Functional Materials and Beyond
|
| Presentation Title |
Computationally Guided High Entropy Alloy Discovery |
| Author(s) |
John A. Sharon, Ryan Deacon, Soumalya Sarkar, Kenneth Smith |
| On-Site Speaker (Planned) |
John A. Sharon |
| Abstract Scope |
High entropy alloys (HEA), composed of multiple principal elements, have been demonstrated to have enhanced properties compared to conventional systems. While this class of materials holds exciting potential for numerous industrial and aerospace applications the option space of possible HEAs is still largely unexplored as billions of compositions exist for alloys that contain 4 or more elements. This talk will highlight a machine learning-based framework which is able to converge on a set of HEA candidates given a large set of design objectives and constraints. The proposed approach demonstrates scalability to comprehensive HEA space exploration even while receiving data from variably expensive physics-based thermo-mechanical models. Data from test coupons as well as the open literature can also be incorporated into this framework to aid in the reliability of the overall framework. Examples of framework prediction with corresponding experimental validation will also be discussed. |