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.
| Original language | English |
|---|---|
| Pages (from-to) | 916-930 |
| Number of pages | 15 |
| Journal | Journal of Phase Equilibria and Diffusion |
| Volume | 43 |
| Issue number | 6 |
| DOIs | |
| State | Published - Dec 2022 |
Funding
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.
Keywords
- CALPHAD approach
- binary system
- gibbs energy
- neural network