About this Abstract |
Meeting |
2nd World Congress on High Entropy Alloys (HEA 2021)
|
Symposium
|
2nd World Congress on High Entropy Alloys (HEA 2021)
|
Presentation Title |
ON DEMAND: Predicting Fundamental Properties of bcc Refractory Multicomponent Alloys Using Electronic Descriptors and Statistical Learning |
Author(s) |
Yong-Jie Hu, Christopher Tandoc, Liang Qi |
On-Site Speaker (Planned) |
Yong-Jie Hu |
Abstract Scope |
Optimizing chemistries of bcc refractory multicomponent alloys to achieve a synergy of high strength and low-temperature ductility requires reliable predictions of the correlated alloy properties across a vast compositional space. In this work, first-principles calculations were performed for 106 individual bcc solid-solution alloys to predict several strength/ductility-related fundamental alloy properties, including lattice distortions, unstable stacking fault energies, and surface energies. With the descriptors based on electronic structures of interatomic bonding, several statistical learning models were developed to efficiently and accurately predict the formation energies of these planar defects and magnitudes of lattice distortions according to alloying compositions. The developed statistical models further enabled rapid and systematic search of potential alloy candidates that are intrinsically ductile and with high yield strengths across high-order multicomponent systems. |
Proceedings Inclusion? |
Undecided |