| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Understanding High Entropy Materials via Data Science and Computational Approaches
|
| Presentation Title |
Modeling Distribution of Unstable Stacking Fault Energy in bcc Refractory High-Entropy Alloys and its Implication to Ductility Assessment |
| Author(s) |
Yong-Jie Hu, Christopher Tandoc |
| On-Site Speaker (Planned) |
Yong-Jie Hu |
| Abstract Scope |
Body-centered cubic (bcc) refractory high entropy alloys (RHEA) are of great interest due to their remarkable strength at high temperatures. Optimizing the chemical compositions of these alloys to achieve a combination of high strength and room-temperature ductility remains challenging. We previously introduced a machine learning model that was able to produce high throughput estimations of RHEA unstable stacking fault and surface energies that can be used in a Rice model of crack-tip deformation to predict intrinsic ductility. We build on this work by introducing the capability to model local chemical fluctuations. Probability theory is used to calculate the prevalence of a given sub-composition in a bulk random alloy. The calculated prevalence is paired with unstable stacking fault energy estimates from the aforementioned machine learning model to model the distribution of this defect energy in random alloys. The implications on ductility are investigated against experimental tensile testing data from the literature. |