A first-principles-based high fidelity, high throughput approach for the design of high entropy alloys.
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ABSTRACT:
SUBMITTER: Sorkin V
PROVIDER: S-EPMC9279411 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this work, the authors have demonstrated the use of machine learning (ML) models in the prediction of bulk modulus for High Entropy Alloys (HEA). For the first time, ML has been used for optimizing the composition of HEA to achieve enhanced bulk modulus values. A total of 12 ML algorithms were trained to classify the elemental composition as HEA or non-HEA. Among these models, Gradient Boosting Classifier (GBC) was found to be the most accurate, with a test accuracy of 78%. Further, six regression models were trained to predict the bulk modulus of HEAs, and the best results were obtained by LASSO Regression model with an R-square value of 0.98 and an adjusted R-Square value of 0.97 for the test data set. This work effectively bridges the gap in the discovery and property analysis of HEAs. By accelerating material discovery via providing alternate means for designing virtual alloy compositions having favourable bulk modulus for respective applications, this work opens new avenues of applications of HEAs.
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