A Novel Data-Driven Emulator for Predicting Electromigration-Mediated Damage in Polycrystalline Interconnects

Wu, Peichen; Farmer, W. A.; Iquebal, Ashif Sikandar; Ankit, Kumar

doi:10.1007/s11664-023-10237-9

Cited by 2 publications

(2 citation statements)

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“…Another work presented by Wu et al shows the capacity of DL models for predicting microstructural evaluation (Figure 10b). [157] The DL framework combines CNN and RNN models, in which CNN extracts features and spatial relationships in each image and RNN outputs temporal relationship between the subsequent images. The dataset composes phase-field generated microstructures using spinodal decomposition.…”

Section: Predicting Effective Propertiesmentioning

confidence: 99%

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Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

et al. 2023

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Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring their material and geometric distribution unlocks the potential to achieve unprecedented bulk properties and functions. However, current mechanical metamaterial design considerably relies on experienced designers' inspiration through trial and error, while investigating their mechanical properties and responses entails time‐consuming mechanical testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized the design process of mechanical metamaterials, enabling property prediction and geometry generation without prior knowledge. Furthermore, deep generative models can transform conventional forward design into inverse design. Many recent studies on the implementation of deep learning in mechanical metamaterials are highly specialized, and their pros and cons may not be immediately evident. This critical review provides a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, this review highlights the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence. This article is expected to be valuable not only to researchers working on mechanical metamaterials but also those in the field of materials informatics.This article is protected by copyright. All rights reserved

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Section: Predicting Effective Propertiesmentioning

confidence: 99%

“…b) Mapping the first ten phase separating microstructures into the subsequent 30 evolutionary microstructures using DL framework combining CNN and RNN models. Reproduced with permission [157]. Copyright 2023, Elsevier.…”

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confidence: 99%