Abstract
We developed a PyTorch-based architecture called HydraGNN that implements graph convolutional neural networks (GCNNs) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN surrogate model on a dataset for nickel–niobium (NiNb) generated by the embedded atom model (EAM) empirical interatomic potential for demonstration purposes. The dataset was generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structures. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as function of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys.
Original language | English |
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Article number | 112141 |
Journal | Computational Materials Science |
Volume | 224 |
DOIs | |
State | Published - May 2023 |
Funding
The authors thank Dr. Vladimir Protopopescu for his valuable feedback in the preparation of this manuscript. This work was supported in part by the Office of Science of the Department of Energy, United States and by the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory (ORNL), United States . This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, United States , managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725 . This work used resources of the Oak Ridge Leadership Computing Facility (OLCF) and of the Edge Computing program at ORNL, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 . An award of computer time was provided by the OLCF Director’s Discretion Project program using the OLCF award MAT250. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).
Keywords
- Bulk modulus
- Condensed matter
- Deep learning
- Graph neural networks
- Solid solution alloys