Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials
Using a large-scale, experimentally captured 3D microstructure data set, 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 realisti...
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| Published in: | JOM (1989) Vol. 73; no. 1; pp. 90 - 102 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Springer US
01.01.2021
Springer Nature B.V Springer |
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| ISSN: | 1047-4838, 1543-1851 |
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| Abstract | Using a large-scale, experimentally captured 3D microstructure data set, 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. |
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| AbstractList | 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. Finally, 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. Using a large-scale, experimentally captured 3D microstructure data set, 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. |
| Author | Abernathy, Harry W. Epting, William K. Holm, Elizabeth A. Hackett, Gregory A. Salvador, Paul A. Kim, Hokon Hsu, Tim Rollett, Anthony D. |
| Author_xml | – sequence: 1 givenname: Tim surname: Hsu fullname: Hsu, Tim organization: US DOE National Energy Technology Laboratory, Materials Science and Engineering, Carnegie Mellon University, Lawrence Livermore National Laboratory – sequence: 2 givenname: William K. surname: Epting fullname: Epting, William K. organization: US DOE National Energy Technology Laboratory, Leidos Research Support Team – sequence: 3 givenname: Hokon surname: Kim fullname: Kim, Hokon organization: US DOE National Energy Technology Laboratory, Materials Science and Engineering, Carnegie Mellon University – sequence: 4 givenname: Harry W. surname: Abernathy fullname: Abernathy, Harry W. organization: US DOE National Energy Technology Laboratory, Leidos Research Support Team – sequence: 5 givenname: Gregory A. surname: Hackett fullname: Hackett, Gregory A. organization: US DOE National Energy Technology Laboratory – sequence: 6 givenname: Anthony D. surname: Rollett fullname: Rollett, Anthony D. organization: US DOE National Energy Technology Laboratory, Materials Science and Engineering, Carnegie Mellon University – sequence: 7 givenname: Paul A. surname: Salvador fullname: Salvador, Paul A. organization: US DOE National Energy Technology Laboratory, Materials Science and Engineering, Carnegie Mellon University – sequence: 8 givenname: Elizabeth A. orcidid: 0000-0003-3064-5769 surname: Holm fullname: Holm, Elizabeth A. email: eaholm@andrew.cmu.edu organization: US DOE National Energy Technology Laboratory, Materials Science and Engineering, Carnegie Mellon University |
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| SubjectTerms | Algorithms Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification Chemistry/Food Science Datasets Earth Sciences Electrochemical analysis Engineering Environment Finite element method Generative adversarial networks Machine learning MATERIALS SCIENCE Mathematical models Microstructure Neural networks Particle size Physics Simulation Solid oxide fuel cells Tortuosity |
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| Title | Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials |
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