| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Using Machine-learning Potentials for Free Energy Calculations of Multicomponent Alloys |
| Author(s) |
Prashanth Srinivasan, Yuji Ikeda, Blazej Grabowski, Jan Janssen, Alexander Shapeev, Jörg Neugebauer, Fritz Körmann |
| On-Site Speaker (Planned) |
Prashanth Srinivasan |
| Abstract Scope |
Accurate and efficient computational schemes to predict parameter-free free energies are crucial in computational materials design. A large contribution stems from the vibrational free energy, which determination, including anharmonic contributions is, in general, challenging. We present a scheme which is equally applicable from unaries to concentrated solid solutions (also high entropy alloys) to derive highly accurate vibrational free energies including explicit anharmonic contributions. A machine-learnt moment tensor potential [Shapeev, 2016] is built serving as a highly efficient reference potential for sampling the atomic phase space. The potential is then used as a part of a thermodynamic integration to compute accurate free energies. The scheme is applied to 17 refractory element-based systems ranging from unaries up to five-component high entropy alloys to study the impact of configurational entropy on the total free energy and the vibrational (and anharmonic) contribution. The workflow is implemented in http://pyiron.org to enhance its dissemination and reuse. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |