| About this Abstract |
| Meeting |
3rd World Congress on High Entropy Alloys (HEA 2023)
|
| Symposium
|
HEA 2023
|
| Presentation Title |
Machine Learning Guided High Entropy Alloy Development |
| Author(s) |
John Sharon, Ken Smith, Ryan Deacon, Anthony Ventura, Soumalya Sarkar, GV Srinivasan |
| On-Site Speaker (Planned) |
Ryan Deacon |
| Abstract Scope |
High Entropy Alloys (HEAs) with multiple principal elements have demonstrated enhanced properties that can rival or exceed conventional alloy systems. HEAs are typically comprised of 4 or more elements present from 5 to 35 at.% resulting in a large combinatorial composition space for which computational tools are vital to sort through combinations and identify the most promising candidates. A variety of analytical and other relatively fast computational models are available to help identify candidates. This talk will describe a machine learning framework assembled to assist in identifying candidates that leverages experimental data, published literature, as well as mechanistic models. Examples of using the framework to identify potential HEA candidates will be provided along with complementary experimental characterization and validation. Industry perspective on HEA maturation and adoption for engineering applications will also be discussed |
| Proceedings Inclusion? |
Planned: Metallurgical and Materials Transactions |