An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties

Abstract

The high permeability and strong selectivity of nanoporous silicon nitride (NPN) membranes make them attractive in a broad range of applications. Despite their growing use, the strength of NPN membranes needs to be improved for further extending their biomedical applications. In this work, we implement a deep learning framework to design NPN membranes with improved or prescribed strength values. We examine the predictions of our framework using physics-based simulations. Our results confirm that the proposed framework is not only able to predict the strength of NPN membranes with a wide range of microstructures, but also can design NPN membranes with prescribed or improved strength. Our simulations further demonstrate that the microstructural heterogeneity that our framework suggests for the optimized design, lowers the stress concentration around the pores and leads to the strength improvement of NPN membranes as compared to conventional membranes with homogenous microstructures.

Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review

“…90 GAN has also been employed to solve inverse design problems as it can find a new design candidate with excellent predictive performance, a design that is still similar to the designs within the original training set. [91][92][93] Additionally, modified forms of GANs, such as conditional GAN (CGAN) 94 and Wasserstein GAN (WGAN), 95 further expanded the scope of inverse design by making the training of the model easier and expanding the types of tasks that the DL can perform. [96][97][98] For example, Kim et al (2020) applied WGAN structure to build a network called ZeoGAN and solved the inverse design problem of porous material to obtain the desired level of methane heat absorption.…”

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

“…In recent years, deep learning models and convolutional neural networks (CNNs) have emerged as powerful tools for field predictions in the domains of engineering and material science [1,2]. The potential of deep learning to uncover intricate patterns and spatial dependencies within complex datasets has enabled end-to-end field prediction outputs such as damage, stress, and strain from image datasets of material microstructures [3][4][5][6] or heterogeneous geometries [7][8][9][10]. Data-driven models trained using computer vision and semantic segmentation techniques [11] have utilized datasets with both paired [12,13] and unpaired [14,15] images from physics-informed simulation approaches like molecular dynamics (MD) [6,14] and finite element method (FEM) [16][17][18][19].…”

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

“…Their results show that the deep learning approaches (CNN/cGAN) provide highly accurate predictions with significantly reduced mean squared error and high correlation values compared to classical ML methods. Shargh and Abdolrahim [40] developed a CNN network used for predicting the strength of nanoporous silicon nitride membranes based on their microstructures. Sepasdar et al [41] used CNN for the development of an image-based deep learning framework for predicting the nonlinear stress distribution and failure pattern in microstructural representations of composite materials.…”