Inverse machine learning discovered metamaterials with record high recovery stress

Challapalli, Adithya; Konlan, John; Li, Guoqiang

doi:10.1016/j.ijmecsci.2022.108029

Cited by 18 publications

(3 citation statements)

References 57 publications

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“…[ 92 ] An inverse design framework utilizing Spearman correlation analysis and ML regression models was developed and a thin‐walled flexible unit with exceptional stress recovery, load carrying, and energy absorption qualities were generated. [ 93 ] As such, wearable sensor design can greatly benefit from similar strategies to better control design parameters.…”

Section: Applications Of Wearables In Healthcare and Medicinementioning

confidence: 99%

AI‐Based Metamaterial Design for Wearables

Yigci,

Ahmadpour,

Tasoglu

2023

Advanced Sensor Research

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Continuous monitoring of physiological parameters has remained an essential component of patient care. With an increased level of consciousness regarding personal health and wellbeing, the scope of physiological monitoring has extended beyond the hospital. From implanted rhythm devices to non‐contact video monitoring for critically ill patients and at‐home health monitors during Covid‐19, many applications have enabled continuous health monitorization. Wearable health sensors have allowed chronic patients as well as seemingly healthy individuals to track a wide range of physiological and pharmacological parameters including movement, heart rate, blood glucose, and sleep patterns using smart watches or textiles, bracelets, and other accessories. The use of metamaterials in wearable sensor design has offered unique control over electromagnetic, mechanical, acoustic, optical, or thermal properties of matter, enabling the development of highly sensitive, user‐friendly, and lightweight wearables. However, metamaterial design for wearables has relied heavily on manual design processes including human‐intuition‐based and bio‐inspired design. Artificial intelligence (AI)‐based metamaterial design can support faster exploration of design parameters, allow efficient analysis of large data‐sets, and reduce reliance on manual interventions, facilitating the development of optimal metamaterials for wearable health sensors. Here, AI‐based metamaterial design for wearable healthcare is reviewed. Current challenges and future directions are discussed.

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Section: Applications Of Wearables In Healthcare and Medicinementioning

confidence: 99%

AI‐Based Metamaterial Design for Wearables

Yigci,

Ahmadpour,

Tasoglu

2023

Advanced Sensor Research

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“…Similarly, Großmann and Mittelstedt [5] focused on the manufacturing challenges and elastic properties of two-dimensional cellular solids in AM, highlighting the significance of structure geometry and material volume in achieving high lightweight degrees. Furthermore, the development of machine learning-derived graded cellular structures, as proposed by Challapalli, Konlan, and Li [6], underscores the need for computationally efficient design strategies that account for AM constraints like support structures and powder removal. These studies collectively illustrate the critical role of geometric considerations in the AM of cellular structures, paving the way for innovative design methodologies that harness the full potential of AM technologies.…”

Section: Introductionmentioning

confidence: 99%

Geometric modeling of advanced cellular structures with skeletal graphs

Letov,

Zhao

2024

International Journal of Mechanical Sciences

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“…To overcome these issues, various machine learning techniques, including deep learning, have emerged as a powerful tool in the design and optimization of metamaterials Machine learning (ML) techniques offer various applications in the field of metamaterials, such as in the discovery and design of new metamaterial compositions and structures by predicting properties and optimizing material configurations It can also characterize the performance of metamaterials by analyzing experimental or simulation data. , Further, ML algorithms can solve inverse design problems, enabling the engineering of metamaterials with desired electromagnetic or acoustic responses. − ML techniques have also been applied to optimize manufacturing processes to improve robustness and generalization and enhance the overall understanding and advancement of metamaterials. − By leveraging ML, researchers can accelerate the development of unique materials with extraordinary properties for applications in telecommunications, sensing, energy harvesting, and more.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Assisted Design of Multiband Terahertz Metamaterial Absorber

Soni,

Misra

2023

ACS Appl. Opt. Mater.

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The design of metamaterials involves engineering the electromagnetic properties on a subwavelength scale, making their behavior challenging to predict with models that are timeconsuming and computationally expensive. In recent years, there has been growing interest in developing various machine learning (ML) models for designing metamaterials. In this research, we demonstrate an ML-based framework for the optimal design of metamaterial absorbers in the terahertz range. Herein, the optical properties of a metamaterial structure with an open polygonshaped meta-atom are studied using numerical simulations. In addition, machine learning models are developed, specifically, a forward and an inverse model to predict absorption spectra from the structure and vice versa using different machine learning algorithms, including neural networks, decision trees, KNN, and linear regression. The mean square error was as low as 0.003 for the forward model and 0.02 for the inverse model. This work can be expanded for designing other complex fabricated electromagnetic structures for use in metamaterials and metasurfaces.

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Inverse machine learning discovered metamaterials with record high recovery stress

Cited by 18 publications

References 57 publications

AI‐Based Metamaterial Design for Wearables

AI‐Based Metamaterial Design for Wearables

Geometric modeling of advanced cellular structures with skeletal graphs

Machine-Learning-Assisted Design of Multiband Terahertz Metamaterial Absorber

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