Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials

• URL: http://arxiv.org/abs/2006.13886v1
• Date: Mon, 22 Jun 2020 21:52:01 GMT
• Title: Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials
• Authors: Tim Hsu, William K. Epting, Hokon Kim, Harry W. Abernathy, Gregory A. Hackett, Anthony D. Rollett, Paul A. Salvador, and Elizabeth A. Holm
• Abstract summary: We implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. Results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D) 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks [52.13486402193811]
  New solid-state materials require rapidly exploring the vast space of crystal structures and locating stable regions. Existing methods struggle to explore large material spaces and generate diverse samples with desired properties and requirements. We propose a novel generative model employing a hierarchical exploration strategy to efficiently exploit the symmetry of the materials space to generate crystal structures given desired properties.
  arXiv  Detail & Related papers  (2024-11-06T23:53:34Z)
• DPLM-2: A Multimodal Diffusion Protein Language Model [75.98083311705182]
  We introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures. DPLM-2 learns the joint distribution of sequence and structure, as well as their marginals and conditionals. Empirical evaluation shows that DPLM-2 can simultaneously generate highly compatible amino acid sequences and their corresponding 3D structures.
  arXiv  Detail & Related papers  (2024-10-17T17:20:24Z)
• A Generative Machine Learning Model for Material Microstructure 3D Reconstruction and Performance Evaluation [4.169915659794567]
  The dimensional extension from 2D to 3D is viewed as a highly challenging inverse problem from the current technological perspective. A novel generative model that integrates the multiscale properties of U-net with and the generative capabilities of GAN has been proposed. The model's accuracy is further improved by combining the image regularization loss with the Wasserstein distance loss.
  arXiv  Detail & Related papers  (2024-02-24T13:42:34Z)
• Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs [0.0]
  We present a framework for reconstruction of anisotropic microstructures based on conditional diffusion-based generative models (DGMs) The proposed framework involves spatial connection of multiple 2D conditional DGMs, each trained to generate 2D microstructure samples for three different planes. The results demonstrate that the framework is capable of reproducing not only the statistical distribution of material phases but also the material properties in 3D space.
  arXiv  Detail & Related papers  (2023-12-13T01:36:37Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN [0.0]
  3D microstructural datasets are commonly used to define the geometrical domains used in finite element modelling. Machine learning method, SliceGAN, was developed to statistically generate 3D microstructural datasets of arbitrary size. We present the results from applying SliceGAN to 87 different microstructures, ranging from biological materials to high-strength steels.
  arXiv  Detail & Related papers  (2022-10-12T19:13:28Z)
• Neural Wavelet-domain Diffusion for 3D Shape Generation [52.038346313823524]
  This paper presents a new approach for 3D shape generation, enabling direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose a compact wavelet representation with a pair of coarse and detail coefficient volumes to implicitly represent 3D shapes via truncated signed distance functions and multi-scale biorthogonal wavelets.
  arXiv  Detail & Related papers  (2022-09-19T02:51:48Z)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics [77.34726150561087]
  This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
  arXiv  Detail & Related papers  (2022-05-31T13:26:51Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials [0.0]
  Multi-scale predictive models of chemical reactions account for the physics at the meso-scale. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. We propose utilizing generative adversarial networks (GAN) to spawn ensembles of synthetic heterogeneous energetic material microstructures.
  arXiv  Detail & Related papers  (2020-04-05T16:58:31Z)
• Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries [0.0]
  This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic n-phase microstructural data. A comparison between the real and synthetic data is performed in terms of the morphological properties. By modifying the input to the generator, we show that it is possible to generate microstructure with periodic boundaries in all three directions.
  arXiv  Detail & Related papers  (2020-02-17T17:38:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.