Microstructure reconstruction using diffusion-based generative models

Microstructure reconstruction has been an essential part of computational material engineering to reveal the relationship between the microstructures and the material properties. However, it is still challenging to find a general solution for microstructure characterization and reconstruction (MCR) tasks although there have been many attempts such as the descriptor-based reconstruction methods. To address this generality problem, the denoising diffusion probabilistic models are first employed for the microstructure reconstruction task which can be applied to various types of material systems. Several microstructures (e.g., carbonate, ceramics, copolymer, etc.) are considered to be reproduced for validating the proposed models while addressing the quality of the generated images with the quantitative evaluation metrics (FID score, precision and recall). The results show that the proposed diffusion model based approach is applicable for reproducing various types of microstructures with different spatial distributions of morphological features. The present approach also provides a stable training procedure with simple implementation for generating visually similar microstructures (and also statistically equivalent) without requiring expert knowledge and some time-consuming parametric studies. The proposed approach has the potential of being a universal microstructure reconstruction method for handling complex microstructures for materials science

Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling

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 for 2D-to-3D reconstruction of microstructures called Micro3Diff 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 new 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 and 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

A low-cost Lactobacillus salivarius L29 growth medium containing molasses and corn steep liquor allows the attainment of high levels of cell mass and lactic acid production

The aim of the present work was to formulate a Lactobacillus salivarius L29 industrial fermentation medium. High cell numbers and good levels of lactic acid by a L. salivarius L29 were obtained after shake flask fermentation using molasses as the sole carbon source and corn steep liquor (CSL (industrial grade); an organic source of N) as the principal nitrogen source. The optimum concentrations of molasses and CSL facilitating good cell growth and high-level lactic acid production were found to be 6 and 6% (both v/v), respectively. The maximum cell yield was 2.02 × 109 CFU/mL, thus about 15% lower than that obtained when MRS broth was employed for 5-L fermenters culture. Lactic acid production upon growth in industrial broth was 105 g/L; the total sugar content of the medium was 118 g/L (sucrose: glucose: fructose 68:14:18; w/w/w). Upon growth in De Man, Rogosa and Sharpe (MRS) broth (the total sugar content of which was 127 g/L, all of which was glucose), the lactic acid yield was 120 g/L. The optimized industrial growth medium was significantly more economical than were conventional broths.Keywords: Lactobacillus salivarius L29, molasses, corn steep liquor, culture medium optimization, lactic acidAfrican Journal of Biotechnology Vol. 12(16), pp. 2013-201

Microstructure Design of Multifunctional Particulate Composite Materials using Conditional Diffusion Models

This paper presents a novel modeling framework to generate an optimal microstructure having ultimate multifunctionality using a diffusion-based generative model. In computational material science, generating microstructure is a crucial step in understanding the relationship between the microstructure and properties. However, using finite element (FE)-based direct numerical simulation (DNS) of microstructure for multiscale analysis is extremely resource-intensive, particularly in iterative calculations. To address this time-consuming issue, this study employs a diffusion-based generative model as a replacement for computational analysis in design optimization. The model learns the geometry of microstructure and corresponding stress contours, allowing for the prediction of microstructural behavior based solely on geometry, without the need for additional analysis. The focus on this work is on mechanoluminescence (ML) particulate composites made with europium ions and dysprosium ions. Multi-objective optimization is conducted based on the generative diffusion model to improve light sensitivity and fracture toughness. The results show multiple candidates of microstructure that meet the design requirements. Furthermore, the designed microstructure is not present in the training data but generates new morphology following the characteristics of particulate composites. The proposed approach provides a new way to characterize a performance-based microstructure of composite materials