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Summary of Efforts in Phase Prediction of High Entropy Alloys Using Machine Learning

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Low Cost Manufacturing Technologies (NERC 2022)

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Abstract

High-entropy alloys (HEAs) possess vast compositional space making them a suitable type of metallic alloy material that can be customized for a wide range of engineering applications ranging from structural, catalytic, functional, hydrogen storage and metamaterials. Predicting the phase of an HEA for a given composition in a certain molar ratio is a daunting task, and hitherto, trial-and-error approaches are employed. With the emergence of data-driven machine learning (ML) technique newer avenues have emerged to reduce the complexity in this task. In this work, we provide a canon of research in this area and used a testbed study by deploying random forest classifier (RFC) to predict distinct phases of HEAs, such as intermetallic (IM), BCC solid-solution (BCC_SS), FCC solid-solution (FCC_SS), and mixed (FCC + BCC) phase. With an average accuracy of 86%, a ROC_AUC score of 0.965, and tenfold cross-validation ROC_AUC score of 0.903, the random forest model showed great ability and prospects in future discovery of novel phases of HEAs. Based on this analysis, the input parameters such as the mixing enthalpy (ΔHmix) and valence electron concentration (VEC) were identified most influential in governing the stable phase of an HEA.

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Acknowledgements

Swati Singh greatly acknowledge the scholarship provided by the Ministry of Education, Government of India. Saurav Goel greatly acknowledge the support provided by the Royal Academy of Engineering via Grants No. IAPP18-19\295 and TSP1332.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Swati Singh, Shrikrishna Nandkishor Joshi & Saurav Goel

  2. School of Engineering, London South Bank University, 103 Borough Road, London, SE1 0AA, UK

    Saurav Goel

  3. Department of Mechanical Engineering, University of Petroleum and Energy Studies, Dehradun, 248007, India

    Saurav Goel

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  1. Swati Singh
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  3. Saurav Goel
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Corresponding author

Correspondence to Shrikrishna Nandkishor Joshi .

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Editors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Shrikrishna Nandkishor Joshi

  2. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Uday Shanker Dixit

  3. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    R. K. Mittal

  4. Mechanical Engineering Department, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Swarup Bag

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Singh, S., Joshi, S.N., Goel, S. (2023). Summary of Efforts in Phase Prediction of High Entropy Alloys Using Machine Learning. In: Joshi, S.N., Dixit, U.S., Mittal, R.K., Bag, S. (eds) Low Cost Manufacturing Technologies. NERC 2022. Springer, Singapore. https://doi.org/10.1007/978-981-19-8452-5_4

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  • DOI: https://doi.org/10.1007/978-981-19-8452-5_4

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