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Prediction of the Composition and Hardness of High-Entropy Alloys by Machine Learning

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Abstract

Machine learning with artificial neural network (ANN)-based methods is a powerful tool for the prediction and exploitation of the subtle relationships between the composition and properties of materials. This work utilizes an ANN to predict the composition of high-entropy alloys (HEAs) based on non-equimolar AlCoCrFeMnNi in order to achieve the highest hardness in the system. A simulated annealing algorithm is integrated with the ANN to optimize the composition. A bootstrap approach is adopted to quantify the uncertainty of the prediction. Without any guidance, the design of new compositions of AlCoCrFeMnNi-based HEAs would be difficult by empirical methods. This work successfully demonstrates that, by applying the machine learning method, new compositions of AlCoCrFeMnNi-based HEAs can be obtained, exhibiting hardness values higher than the best literature value for the same alloy system. The correlations between the predicted composition, hardness, and microstructure are also discussed.

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Acknowledgements

The authors would like to acknowledge support from the Ministry of Science and Technology (MOST) in Taiwan (Projects MOST106-2923-E-007 -002 -MY2, MOST107-2218-E-007 -012 and MOST107-2221-E-492 -011 -MY3), and the “High Entropy Materials Center” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) and from the Project MOST 107-3017-F-007-003 by MOST.

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Authors and Affiliations

  1. Department of Material Science and Engineering, National Tsing Hua University, Hsinchu City, Taiwan, ROC

    Yao-Jen Chang & An-Chou Yeh

  2. High Entropy Materials Center, National Tsing Hua University, Hsinchu City, Taiwan, ROC

    Yao-Jen Chang & An-Chou Yeh

  3. National Center for High-Performance Computing, Hsinchu City, Taiwan, ROC

    Chia-Yung Jui & Wen-Jay Lee

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  1. Yao-Jen Chang
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Chang, YJ., Jui, CY., Lee, WJ. et al. Prediction of the Composition and Hardness of High-Entropy Alloys by Machine Learning. JOM 71, 3433–3442 (2019). https://doi.org/10.1007/s11837-019-03704-4

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  • DOI: https://doi.org/10.1007/s11837-019-03704-4

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