microstructure generation via generative adversarial network for heterogeneous topologically complex 3d materials
FOS: Computer and information sciences
Condensed Matter - Materials Science
Computer Vision and Pattern Recognition (cs.CV)
Image and Video Processing (eess.IV)
Computer Science - Computer Vision and Pattern Recognition
FOS: Electrical engineering, electronic engineering, information engineering
Materials Science (cond-mat.mtrl-sci)
FOS: Physical sciences
02 engineering and technology
Electrical Engineering and Systems Science - Image and Video Processing
0210 nano-technology
01 natural sciences
0104 chemical sciences
DOI:
10.48550/arxiv.2006.13886
Publication Date:
2020-12-02
AUTHORS (16)
ABSTRACT
Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. The ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.<br/>submitted to JOM<br/>
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