|About this Abstract
||3rd World Congress on High Entropy Alloys (HEA 2023)
||Accelerating the Discovery of Low-Energy Structure Configurations: A Computational Approach that Integrates First-principles Calculations, Monte Carlo Sampling, and Machine Learning
||Md Rajib Khan Musa, Yichen Qian, Dr. David Cereceda
|On-Site Speaker (Planned)
||Md Rajib Khan Musa
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.
||Planned: Metallurgical and Materials Transactions