| About this Abstract |
| Meeting |
3rd World Congress on High Entropy Alloys (HEA 2023)
|
| Symposium
|
HEA 2023
|
| Presentation Title |
Accelerating the Discovery of Low-Energy Structure Configurations: A Computational Approach that Integrates First-principles Calculations, Monte Carlo Sampling, and Machine Learning |
| Author(s) |
Md Rajib Khan Musa, Yichen Qian, Dr. David Cereceda |
| On-Site Speaker (Planned) |
Md Rajib Khan Musa |
| Abstract Scope |
In this work, we developed a novel and highly efficient computational approach that combines MC sampling, DFT calculations, and Machine Learning (ML) techniques to accelerate the discovery of low-energy structure configurations of alloys. Our method is inspired by the well-established cluster expansion technique, leveraging its strengths while addressing its limitations. Specifically, we enhanced the reliability of the cluster expansion by avoiding out-of-sample prediction using machine learning. We performed first-principle Density Functional Theory (DFT) calculations for those samples. We applied our novel approach to several tungsten-based alloys. Our results show a noteworthy reduction in root mean square error (RMSE) compared to cluster expansion, suggesting its superior accuracy and reliability. |
| Proceedings Inclusion? |
Planned: Metallurgical and Materials Transactions |