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Design of high bulk moduli high entropy alloys using machine learning - Scientific Reports

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Design of high bulk moduli high entropy alloys using machine learning - Scientific Reports

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%.

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Design of high bulk moduli high entropy alloys using machine learning - Scientific Reports

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Design of high bulk moduli high entropy alloys using machine learning - Scientific Reports

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