| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
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| Symposium
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ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets
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| Presentation Title |
Graph Convolutional Neural Networks for Fast, Accurate Prediction of Material Properties for Solid Solution High Entropy Alloys Using Open-source Datasets |
| Author(s) |
Massimiliano (Max) Lupo Pasini, Samuel Reeve, Pei Zhang, Marko Burcul |
| On-Site Speaker (Planned) |
Massimiliano (Max) Lupo Pasini |
| Abstract Scope |
We present numerical results from training two DL models - multi-layer perceptron (MLP) and graph convolutional neural networks (GCNN) - on three different open-source data - ab-initio DFT data for high entropy alloys (HEA) copper-gold (CuAu), iron-platinum (FePt), and silicon-steel (FeSi). This data has been generated for varying sizes of the lattice structure by running the LSMS-3 code, which implements a locally self-consistent multiple scattering method on OLCF supercomputer Summit.
We found that: (i) multi-headed MLPs simultaneously provide accurate and robust estimate of multiple physical properties for CuAu and FePt where the volume of data is sufficiently large (ii) GCNNs attain higher accuracy on the CuAu and FePt datasets with respect to MLP because they take advantage of the topology of the lattice, and (iii) as expected, the volume of data needed by the two DL models to provide reasonably accurate results increases with the size of the lattice. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Computational Materials Science & Engineering, High-Entropy Alloys |