Graph-Based Data Analysis for Building Chemistry–Phase Design Rules for High Entropy Alloys
The number and types of phases formed in high entropy alloys (HEAs) have significant impacts on the mechanical properties. While various machine learning approaches were developed for predicting whether an HEA is single or multiphase, changes in chemistry and/or composition can lead to other changes...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Crystals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4352/15/1/23 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The number and types of phases formed in high entropy alloys (HEAs) have significant impacts on the mechanical properties. While various machine learning approaches were developed for predicting whether an HEA is single or multiphase, changes in chemistry and/or composition can lead to other changes across length scales, which affect material performance. To address this challenge, we introduce a graph-based approach, which captures the similarity of alloys across these length scales, and which defines design pathways for the chemical modifications of alloys. Our network defines different regimes of alloys and therefore allows one to design within the same material regime. This approach, which also provides a new genre of HEA phase diagrams, enhances the design of alloys through control of the phase(s) present while maintaining other relevant alloy properties. |
|---|---|
| ISSN: | 2073-4352 |