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This paper proposes a performance-based microstructure design methodology specifically tailored for gas diffusion layers (GDLs). The generative machine learning denoising diffusion probabilistic model (DDPM) is trained and used for performance-based microstructure design. This study demonstrates significant progress in performance-based design by incorporating DDPM into the microstructure design process, offering a promising approach for GDL microstructure design. This methodology provides the advantage of generating 2D microstructures with desired permeability and volume fractions. Moreover, it is not limited to a specific performance criterion, making it adaptable to target other metrics. To train the DDPM, a 2D GDL microstructure dataset is constructed using the Lattice Boltzmann Method (LBM) for permeability estimation. Subsequently, we employ the DDPM with U-net architecture, which leverages positional encoding to learn the microstructure's volume fraction and permeability effectively. The input label pair of permeability and volume fraction is generated considering the inherent relationship between these two parameters to ensure the generation of meaningful microstructures. This relationship is supposed to ensure that the resulting microstructures align with realistic and physically meaningful characteristics. The simulated performance results obtained from the generated microstructures using the proposed methodology demonstrate a strong consistency with the targeted performance objectives. [ABSTRACT FROM AUTHOR]

This paper proposes a microstructure reconstruction framework with denoising diffusion models for the first time. The novelty and strength of the proposed model lie in its universality and generality for the microstructure characterization and reconstruction (MCR) that can be applied to various types of composite materials. The applicability of the diffusion-based models is validated with several types of microstructures (e.g., polycrystalline alloy, carbonate, ceramics, copolymer, fiber composite, etc.) that have different morphological characteristics. Moreover, an implicit probabilistic model (which yields non-Markovian diffusion processes) is formulated to accelerate the sampling process, thereby controlling the computational cost considering the practicability and reliability. [ABSTRACT FROM AUTHOR]

Acquiring reliable microstructure datasets is a pivotal step toward the systematic design of materials with the aid of integrated computational materials engineering (ICME) approaches. However, obtaining three-dimensional (3D) microstructure datasets is often challenging due to high experimental costs or technical limitations, while acquiring two-dimensional (2D) micrographs is comparatively easier. To deal with this issue, this study proposes a novel framework called 'Micro3Diff' for 2D-to-3D reconstruction of microstructures using diffusion-based generative models (DGMs). Specifically, this approach solely requires pre-trained DGMs for the generation of 2D samples, and dimensionality expansion (2D-to-3D) takes place only during the generation process (i.e., reverse diffusion process). The proposed framework incorporates a concept referred to as 'multi-plane denoising diffusion', which transforms noisy samples (i.e., latent variables) from different planes into the data structure while maintaining spatial connectivity in 3D space. Furthermore, a harmonized sampling process is developed to address possible deviations from the reverse Markov chain of DGMs during the dimensionality expansion. Combined, we demonstrate the feasibility of Micro3Diff in reconstructing 3D samples with connected slices that maintain morphologically equivalence to the original 2D images. To validate the performance of Micro3Diff, various types of microstructures (synthetic or experimentally observed) are reconstructed, and the quality of the generated samples is assessed both qualitatively and quantitatively. The successful reconstruction outcomes inspire the potential utilization of Micro3Diff in upcoming ICME applications while achieving a breakthrough in comprehending and manipulating the latent space of DGMs. [ABSTRACT FROM AUTHOR]

Integrated computational materials engineering (ICME) has significantly enhanced the systemic analysis of the relationship between microstructure and material properties, paving the way for developing high-performance materials. However, analyzing microstructure-sensitive material behavior remains challenging due to the scarcity of three-dimensional (3D) microstructure datasets. Moreover, this challenge is intensified if the microstructure is anisotropic, as this also results in anisotropic material properties. This paper presents a framework for reconstructing anisotropic microstructures solely based on two-dimensional (2D) micrographs using conditional diffusion-based generative models (DGMs). The proposed framework involves the spatial connection of multiple 2D conditional DGMs, each trained to generate 2D microstructure samples for three orthogonal planes. The connected multiple reverse diffusion processes then enable effective modeling of a Markov chain for transforming noise into a 3D microstructure sample. Furthermore, a modified harmonized sampling enhances the sample quality while preserving the spatial connection between the slices of anisotropic microstructure samples in 3D space. The 2D-to-3D reconstructed anisotropic microstructure samples are evaluated regarding the spatial correlation function and the physical material behavior to validate the proposed framework. The results demonstrate that the framework can reproduce the statistical distribution of material phases and properties in 3D space. It highlights the potential application of the proposed 2D-to-3D reconstruction framework in establishing microstructure-property linkages, which could aid high-throughput material design for future studies. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a new material design method by microstructure topology optimization. Novelty of the proposed method is to target the whole nonlinear volume-averaged effective stress-strain curve of microstructure representative volume element (RVE) rather than aiming specific values such as strength, stiffness or Poisson ratio. J2 plasticity model with a linear isotropic hardening model was chosen for local residuals. Global residuals are computed within nonlinear finite element framework for the topology optimization. Sensitivities of the objective function augmented with the residuals and adjoint response vectors with respect to design variables are derived with details and their numerical computational procedures were also presented. Microstructure topologies showing two different targeted stress-strain curves under uniaxial and biaxial loadings were obtained by using the method of moving asymptotes (MMA) optimization algorithm. Accuracy of the sensitivity computations was verified and numerical examples demonstrated a potential of the proposed method in applications to multiscale topology optimization. • A new topology optimization method of microstructure by targeting nonlinear volume-averaged stress-strain path is proposed. • Derivations of sensitivities by adjoint method and detailed computational algorithm are presented. • We verified the sensitivity of the new objective function with respect to SIMP type design variables. • Numerical examples of nonlinear RVE microstructure topology were demonstrated under uniaxial and biaxial loadings. [ABSTRACT FROM AUTHOR]

This paper introduces a novel integrated microstructure design methodology that replaces the common existing design approaches for multifunctional composites: 1) reconstruction of microstructures, 2) analyzing and quantifying material properties, and 3) inverse design of materials. The problem of microstructure reconstruction is addressed using the diffusion-based generative model (DGM), which is a state-of-the-art generative model formulated with a Markovian diffusion process. Then, the conditional formulation of DGM is introduced for guidance to the embedded desired material properties with a transformer-based attention mechanism, which enables the inverse design of multifunctional composites. Furthermore, a convolutional neural network (CNN)-based surrogate model is utilized to facilitate the prediction of linear/nonlinear material properties for building microstructure-property linkages. Combined, the proposed artificial intelligence-based design framework enables large data processing and database construction that is often not affordable with resource-intensive finite element method (FEM)-based direct numerical simulation (DNS) or iterative reconstruction methods. It is worth noting that the proposed DGM-based methodology is not susceptible to unstable training or mode collapse, which are common issues in generative models that are often difficult to address even with extensive hyperparameter tuning. An example case is presented to demonstrate the effectiveness of the proposed approach, which is designing mechanoluminescence (ML) particulate composites. The results show that the designed ML microstructure samples with the proposed generative and surrogate models meet the multiple design requirements (e.g., volume fraction, elastic constant, and light sensitivity). This assessment demonstrates that the proposed integrated methodology provides an end-to-end solution for practical material design applications. [Display omitted] • An integrated microstructure design framework utilizing the diffusion-based generative model (DGM) is proposed. • The problem of microstructure reconstruction is addressed using the DGM with forward/reverse Markovian diffusion processes. • The conditional formulation of DGM is introduced to enable the inverse design of multifunctional composites. [ABSTRACT FROM AUTHOR]

In this paper, we propose a series of novel algorithms for microstructure reconstruction based on constituent morphologies. The proposed algorithm can generate statistically equivalent representative volume elements (RVE) from shape repository consisting of the actual particles from micro-computed tomography (CT) images. The reconstruction algorithm is also general in that it can adopt any morphological descriptors. CDFs errors of the morphological descriptors between the targeted and reconstructed RVEs are minimized within the proposed algorithmic framework. High reliability of the microstructure reconstructions stems from unique and efficient design of the algorithmic framework. One of the novelties of the proposed algorithm is that it does not rely on time-consuming two-point correlation function (TPCF) calculations while it can replicate distributions keeping realistic morphologies of particles. To verify the proposed reconstruction algorithm, the TPCF and homogenized effective properties were compared between targeted and reconstructed RVEs. Moreover, mechanical responses of the targeted and reconstructed RVEs are evaluated and compared by 3D finite element analyses. [ABSTRACT FROM AUTHOR]