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• W4385347462 abstract "Abstract This paper puts forward 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 using the diffusion-based generative model (DGM). The problem of microstructure reconstruction is addressed using DGM, which is a new state-of-the-art generative model formulated with a forward Markovian diffusion process and the reverse 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. A convolutional neural network (CNN)-based surrogate model is utilized to facilitate the prediction of 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) and iterative reconstruction methods. What is important is that the proposed DGM-based methodology is not susceptible to unstable training or mode collapse, which are common issues in neural network 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 made of europium and dysprosium ions. The results show that the inversely-designed multiple ML microstructure candidates with the proposed generative and surrogate models meet the multiple design requirements (e.g., volume fraction, elastic constant, and light sensitivity). The evaluation of the generated samples' quality and the surrogate models' performance using appropriate metrics are also included. This assessment demonstrates that the proposed integrated methodology offers an end-to-end solution for practical material design applications." @default.
• W4385347462 created "2023-07-29" @default.
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• W4385347462 date "2023-07-28" @default.
• W4385347462 modified "2023-09-23" @default.
• W4385347462 title "A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models" @default.
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• W4385347462 doi "https://doi.org/10.21203/rs.3.rs-3171821/v1" @default.
• W4385347462 hasPublicationYear "2023" @default.
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