Phase prediction and experimental realisation of a new high entropy alloy using machine learning.

ABSTRACT: Nearly ~ 10⁸ types of High entropy alloys (HEAs) can be developed from about 64 elements in the periodic table. A major challenge for materials scientists and metallurgists at this stage is to predict their crystal structure and, therefore, their mechanical properties to reduce experimental efforts, which are energy and time intensive. Through this paper, we show that it is possible to use machine learning (ML) in this arena for phase prediction to develop novel HEAs. We tested five robust algorithms namely, K-nearest neighbours (KNN), support vector machine (SVM), decision tree classifier (DTC), random forest classifier (RFC) and XGBoost (XGB) in their vanilla form (base models) on a large dataset screened specifically from experimental data concerning HEA fabrication using mel

SUBMITTER: Singh S 

PROVIDER: S-EPMC10036487 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature