About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Machine-Learning Force-Field to Develop and Optimize Multi-Component Alloys |
Author(s) |
Anup Pandey |
On-Site Speaker (Planned) |
Anup Pandey |
Abstract Scope |
The empirical molecular dynamics (MD)-based atomistic study of multi-component alloys (MCAs) is limited to fewer candidates due to the lack of accurate force fields. It is almost impossible to explore the vast configurational space of MCAs using computationally demanding ab initio methods such as DFT. Machine-learning learning force-fields (ML-FF) have opened up an avenue for the automated search of novel MCAs with desired properties by combining theory and experiments. ML-FF can be utilized for a high-throughput exploration of the alloy components, thereby being a suitable alternative for conventional force fields that have transferability issues. We will discuss the application of ML-FF in optimizing and developing novel refractory high entropy alloys (RHEAs) with desired mechanical, elastic, and thermal properties. |
Proceedings Inclusion? |
Undecided |