Design of architectured composite materials with an efficient, adaptive artificial neural network-based generative design method

Qian, Chao; Tan, Ren Kai; Ye, Wenjing

doi:10.1016/j.actamat.2021.117548

Cited by 18 publications

(5 citation statements)

References 49 publications

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“…59 There are many other studies that performed optimization by combining the forward modeling network with optimization algorithms in various design problems. [72][73][74][75][76][77][78][79] A different approach called generative inverse design network finds the optimal designs having the desired performance by using back-propagation in neural networks. Generally, backpropagation is a process of optimizing the hyperparameters of hidden layers to minimize the loss function, the value of which quantitatively defines the error between the network's predicted result and the ground truth value.…”

Section: Forward Modeling Network + Optimizationmentioning

confidence: 99%

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

et al. 2023

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Section: Forward Modeling Network + Optimizationmentioning

confidence: 99%

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

et al. 2023

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“…However, it is more desirable to design materials with maximum possible mechanical properties wherein those values are oftentimes outside of the range of the mechanical properties of the training dataset. In this case, further modifications are often needed so that those models can design materials with improved properties that are outside of the range of the properties of the training dataset [35][36][37][38][39][40][41] . Examples of such modifications include considering a large enough training dataset that covers the entire design space, or gradually augmenting the small initial training dataset to the region that contains the optimal design while retraining the network simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the CNN component of our framework guides the GAN component to design NPN membranes with our desired strength. It should be noted that the combined GAN-CNN framework was used in several earlier works for property optimization or image diagnostics 33,[41][42][43][44][45][46][47] . In comparison to earlier works that used GAN-CNN framework, we further add a physics-based interpretation step in the current work which helps to justify the output of our machine learning model based on physics-based simulations.…”

Section: Introductionmentioning

confidence: 99%

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

Shargh

Abdolrahim

2023

npj Comput Mater

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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.

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“…It promises to provide a transformative approach, propelling the process of materials discovery towards unprecedented levels of efficiency and effectiveness. In the meantime, different research articles on specific de-signed materials using AI, involving energy materials [13], composites [14], polymers [15], bioinspired materials [16], and additively manufactured materials [17], are coming out.…”

Section: Introductionmentioning

confidence: 99%

Unleashing the Power of Artificial Intelligence in Materials Design

Badini,

Regondi,

Pugliese

2023

Materials

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The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement.

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Design of architectured composite materials with an efficient, adaptive artificial neural network-based generative design method

Cited by 18 publications

References 49 publications

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

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

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

Unleashing the Power of Artificial Intelligence in Materials Design

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