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

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

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

Disentangling The Effects Of The Employee Benefits On Employee Productivity

This study aimed to investigate the effects of employee benefits on employee productivity. There are conflicting views, positive and negative, with regard to the effect of employee benefits on employee productivity. Overall, we found that employee benefits have a positive impact on employee productivity through the embodied effect (direct effect). Specifically, according to a workplace panel survey in Korea conducted between 2005 and 2009, an increase of one unit in employee benefits leads to an increase of employee productivity by about 7.9%. In addition, we found that such effect is stronger in the manufacturing industry than in the non-manufacturing industry. Although there is no difference in the effect of benefits between large firms and small and medium-sized firms, the labor-embodied effect is stronger in large firms, and the capital-embodied effect is salient in small and medium-sized firms

New Approach of Anti-VEGF Agents for Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is the leading cause of visual loss in older population. Angiogenesis is an important factor associated with the development of CNV due to AMD. Treatment of CNV with intravitreal anti-VEGF monotherapy is currently the standard of care. However, not all patients respond to monotherapy, and modified anti-VEGF treatment regimen and combination therapy may target reducing treatment frequency or improving visual outcome. This paper reviews the many clinical trials that have been performed utilizing several treatment regimens. While many trials have shown that this variable therapy is justifiable, further study is required to determine correct regimens and dosage

Cyclic Loading Test of Structural Walls with Small Openings

This study aimed to investigate the effects of small openings on the structural performance of reinforced-concrete (RC) structural walls. Cyclic lateral loading tests were conducted on five RC walls with an aspect ratio of 2.6 and small openings. The main test parameter was the size of the small openings. The specimens were designed to fail after flexural yielding, considering the typical failure mode of slender RC walls. The structural performances of the test specimens were analyzed based on the test results in terms of the load-carrying capacity (flexural strength), hysteretic behavior, strain distribution, and the size of the openings. The specimens showed flexural yielding regardless of the size of the openings, and the flexural strength and deformation capacity were not significantly affected by the small openings. This result indicates that small openings do not affect the flexural behavior of slender walls if the walls have sufficient shear resistance and the small openings are located away from the extreme compressive end and in the compression zone where compressive stress does not decrease.This work was fnancially supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIT) (No. 2018R1A2B6007559