| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Machine Learning Models for Predictive Materials Science from Fundamental Physics: An Application to Titanium and Zirconium |
| Author(s) |
Mashroor Shafat Shafat Nitol, Doyl Dickel, Christopher Barrett |
| On-Site Speaker (Planned) |
Mashroor Shafat Shafat Nitol |
| Abstract Scope |
Machine learning techniques using rapid artificial neural networks (RANN) have proven to be effective tools to rapidly mimic first principles calculations. New neural network potentials are capable of accurately modeling the transformations between the \alpha,\beta\ and\ \omega phases of titanium and zirconium including accurate prediction of the equilibrium phase diagram. These potentials show remarkable accuracy beyond their first principle dataset, indicating that they reliably parameterize the underlying physics. Transitions between each of the phase pairs are observed in dynamic simulation using calculations of the Gibbs free energy. The calculated triple points are 8.67 GPa, 1058 K for Ti and 5.04 GPa, 988.35 K for Zr, close to their experimentally observed values. The success of the RANN potentials with single element phase transitions suggests the potential of this method to make robust alloy phase diagram calculations such as for TiAl. This can augment or anticipate experiments to accelerate materials discovery. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Phase Transformations, Machine Learning, ICME |