| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
|
| Presentation Title |
Predicting Vibrational Entropy of FCC Solids Uniquely from Bond Chemistry Using Machine Learning |
| Author(s) |
Anus Manzoor, Dilpuneet S. Aidhy |
| On-Site Speaker (Planned) |
Anus Manzoor |
| Abstract Scope |
Despite a well-recognized contribution of vibrational entropy (Svib) in the phase stability of alloys, it remains a peripheral quantity due to its high computational cost. In this work, using a combination of density functional theory (DFT) calculations and machine learning (ML), we show that the expensive Svib computations can be completely circumvented. This is possible because there exists a unique force constant (FC) – bond length relationship for every A-A and A-B bond and the influence of the alloy composition on FCs can be captured with the change in bond lengths only. The DFT database coupled with ML model allows to predict FCs between any two elements which in turn enables predicting Svib of any complex alloy thereby significantly reducing the computational costs. This work opens a new avenue to predict Svib of complex HEAs thereby making Svib as readily available as the mixing enthalpy. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, High-Entropy Alloys, Computational Materials Science & Engineering |