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A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

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Abstract

We present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to predict ternary systems as a function of composition and temperature. We have chosen the energetics of the FCC solid solution phase in 226 binaries consisting of 23 elements at 11 different temperatures to demonstrate the feasibility. The number of binary data points included in the present study is 102,000. We select six ternaries to augment the binary dataset to investigate their influence on the NN prediction accuracy. We examine the sensitivity of data sampling on the prediction accuracy of NNs over selected ternary systems. It is anticipated that the current DL workflow can be further elevated by integrating advanced descriptors beyond the elemental composition and more curated training datasets to improve prediction accuracy and applicability.

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Acknowledgements

This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. MLP would like to thank Vladimir Protopopescu and Sam T. Reeves for their suggestions and comments. DS would like to thank In-Ho Jung for his valuable comments and discussion.

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Authors and Affiliations

  1. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Paul Laiu & Jong Youl Choi

  2. Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Ying Yang & Dongwon Shin

  3. Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Massimiliano Lupo Pasini

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  1. Paul Laiu
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  2. Ying Yang
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  3. Massimiliano Lupo Pasini
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  4. Jong Youl Choi
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Correspondence to Dongwon Shin.

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This invited article is part of a special tribute issue of the Journal of Phase Equilibria and Diffusion dedicated to the memory of former JPED Editor-in-Chief John Morral. The special issue was organized by Prof. Yongho Sohn, University of Central Florida; Prof. Ji-Cheng Zhao, University of Maryland; Dr. Carelyn Campbell, National Institute of Standards and Technology; and Dr. Ursula Kattner, National Institute of Standards and Technology.

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Laiu, P., Yang, Y., Lupo Pasini, M. et al. A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions. J. Phase Equilib. Diffus. 43, 916–930 (2022). https://doi.org/10.1007/s11669-022-01010-2

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