Machine learning approaches for permittivity prediction and rational design of microwave dielectric ceramics

Qin, Jincheng; Liu, Zhifu; Ma, Mengyao; Li, Yongxiang

doi:10.1016/j.jmat.2021.02.012

Cited by 31 publications

(19 citation statements)

References 56 publications

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“…For Cu 3 B 2 O 6 , the average Cu–O bond length is 2.1 Å [ 28 ] . The analysis of ppm , va , and blm values for CuB 2 O 4 and Cu 3 B 2 O 6 leads to the conclusion that these parameters are close to the ranges indicated in [ 41 ] for low permittivity candidate materials.…”

Section: Resultssupporting

confidence: 71%

“…The leads to the conclusion that these parameters are close to the ranges indicated in [41] for low permittivity candidate materials.…”

Section: Dielectric Propertiessupporting

confidence: 62%

“…The average cell volume per atom va is also an important parameter that should be maintained in an optimal range. Qin et al [ 41 ] stated that the ranges of the decisive parameters that favor creating materials with a low dielectric permittivity are ppm < 0.15, va 11–16 Å 3 , and blm < 2.3 Å.…”

Section: Resultsmentioning

confidence: 99%

“…A lower average bond length diminishes the rattling effect of cations in a polyhedral structural unit. A lower cell volume restricts the interaction of polarizable dipoles [41][42][43]. Qin et al [41] proposed a universal model based on machine learning for predicting microwave dielectric permittivity.…”

Section: Dielectric Propertiesmentioning

confidence: 99%

“…A lower cell volume restricts the interaction of polarizable dipoles [41][42][43]. Qin et al [41] proposed a universal model based on machine learning for predicting microwave dielectric permittivity. These authors stated that there are three most important features related to the crystal structure of a compound determining its dielectric permittivity.…”

Section: Dielectric Propertiesmentioning

confidence: 99%

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Sintering, Microstructure, and Dielectric Properties of Copper Borates for High Frequency LTCC Applications

et al. 2021

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New ceramic materials based on two copper borates, CuB2O4 and Cu3B2O6, were prepared via solid state synthesis and sintering, and characterized as promising candidates for low dielectric permittivity substrates for very high frequency circuits. The sintering behavior, composition, microstructure, and dielectric properties of the ceramics were investigated using a heating microscope, X-ray diffractometry, scanning electron microscopy, energy dispersive spectroscopy, and terahertz time domain spectroscopy. The studies revealed a low dielectric permittivity of 5.1–6.7 and low dielectric loss in the frequency range 0.14–0.7 THz. The copper borate-based materials, owing to a low sintering temperature of 900–960 °C, are suitable for LTCC (low temperature cofired ceramics) applications.

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Section: Resultssupporting

confidence: 71%

“…The leads to the conclusion that these parameters are close to the ranges indicated in [41] for low permittivity candidate materials.…”

Section: Dielectric Propertiessupporting

confidence: 62%

Section: Resultsmentioning

confidence: 99%

Section: Dielectric Propertiesmentioning

confidence: 99%

Section: Dielectric Propertiesmentioning

confidence: 99%

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Sintering, Microstructure, and Dielectric Properties of Copper Borates for High Frequency LTCC Applications

et al. 2021

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A machine learning approach for ball milling of alumina ceramics

Raju

Jin

et al. 2022

Int J Adv Manuf Technol

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In this work, machine learning (ML) approach based on polynomial regression was explored to analyze the optimal processing parameters and predict the target particle sizes for ball milling of alumina ceramics. Data points were experimentally obtained by measuring the particle sizes. Prediction interval (PI)-based optimization methods using polynomial regression analysis are proposed. As a first step, functional relations between the inputs and the responses are derived by applying the regression analysis. Later, based on these relations, objective functions to be maximized are defined by desirability approach. Finally, the proposed PI-based methods optimize both parameter points and intervals of the target mill for accomplishing userspecified target responses. The optimization results show that the PI-based point optimization methods can select and recommend statistically reliable optimized parameter points even though unique solutions for the objective functions do not exist. From the results of confirmation experiments, it is established that the optimized parameter points can produce desired particle sizes with quality responses quite similar to the target responses.

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Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Wu,

Xu,

Fang

et al. 2024

Arch Computat Methods Eng

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In the past three decades, biomedical engineering has emerged as a significant and rapidly growing field across various disciplines. From an engineering perspective, biomaterials, biomechanics, and biofabrication play pivotal roles in interacting with targeted living biological systems for diverse therapeutic purposes. In this context, in silico modelling stands out as an effective and efficient alternative for investigating complex interactive responses in vivo. This paper offers a comprehensive review of the swiftly expanding field of machine learning (ML) techniques, empowering biomedical engineering to develop cutting-edge treatments for addressing healthcare challenges. The review categorically outlines different types of ML algorithms. It proceeds by first assessing their applications in biomaterials, covering such aspects as data mining/processing, digital twins, and data-driven design. Subsequently, ML approaches are scrutinised for the studies on mono-/multi-scale biomechanics and mechanobiology. Finally, the review extends to ML techniques in bioprinting and biomanufacturing, encompassing design optimisation and in situ monitoring. Furthermore, the paper presents typical ML-based applications in implantable devices, including tissue scaffolds, orthopaedic implants, and arterial stents. Finally, the challenges and perspectives are illuminated, providing insights for academia, industry, and biomedical professionals to further develop and apply ML strategies in future studies.

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Machine learning approaches for permittivity prediction and rational design of microwave dielectric ceramics

Cited by 31 publications

References 56 publications

Sintering, Microstructure, and Dielectric Properties of Copper Borates for High Frequency LTCC Applications

Sintering, Microstructure, and Dielectric Properties of Copper Borates for High Frequency LTCC Applications

A machine learning approach for ball milling of alumina ceramics

Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

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