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      The latest process and challenges of microwave dielectric ceramics based on pseudo phase diagrams

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          Abstract

          The explosive process of 5G communication evokes the urgent demand of miniaturized and integrated dielectric ceramics filter. It is a pressing need to advance the development of dielectric ceramics utilization of emerging technology to design new materials and understand the polarization mechanism. This review provides the summary of the study of microwave dielectric ceramics (MWDCs) sintered higher than 1000 from 2010 up to now, °C with the purpose of taking a broad and historical view of these ceramics and illustrating research directions. To date, researchers endeavor to explain the structure-property relationship of ceramics with multitude of approaches and design a new formula or strategy to obtain excellent microwave dielectric properties. There are variety of factors that impact the permittivity, dielectric loss, and temperature stability of dielectric materials, covering intrinsic and extrinsic factors. Many of these factors are often intertwined, which can complicate new dielectric material discovery and the mechanism investigation. Because of the various ceramics systems, pseudo phase diagram was used to classify the dielectric materials based on the composition. In this review, the ceramics were firstly divided into ternary systems, and then brief description of the experimental probes and complementary theoretical methods that have been used to discern the intrinsic polarization mechanisms and the origin of intrinsic loss was mentioned. Finally, some perspectives on the future outlook for high-temperature MWDCs were offered based on the synthesis method, characterization techniques, and significant theory developments.

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          Machine-learning-assisted materials discovery using failed experiments.

          Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on 'dark' reactions--failed or unsuccessful hydrothermal syntheses--collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions for new organically templated inorganic product formation with a success rate of 89 per cent. Inverting the machine-learning model reveals new hypotheses regarding the conditions for successful product formation.
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            Virtual screening of inorganic materials synthesis parameters with deep learning

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              Bond susceptibilities and ionicities in complex crystal structures

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                Author and article information

                Contributors
                lienzhu@uestc.edu.cn
                Journal
                J Adv Ceram
                Journal of Advanced Ceramics
                Tsinghua University Press (Beijing )
                2226-4108
                2227-8508
                11 October 2021
                11 October 2021
                2021
                : 10
                : 5
                : 885-932
                Affiliations
                [1 ]GRID grid.54549.39, ISNI 0000 0004 0369 4060, National Engineering Research Center of Electromagnetic Radiation Control Materials, , University of Electronic Science and Technology of China, ; Chengdu, 610054 China
                [2 ]GRID grid.54549.39, ISNI 0000 0004 0369 4060, Key Laboratory of Multi-Spectral Absorbing Materials and Structures of Ministry of Education, , University of Electronic Science and Technology of China, ; Chengdu, 610054 China
                Article
                528
                10.1007/s40145-021-0528-4
                8502792
                4192903c-eff4-4375-a85f-13dba6407084
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

                The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

                To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 April 2021
                : 4 June 2021
                : 6 June 2021
                Categories
                Review
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                © The Author(s) 2021

                high-temperature microwave dielectric ceramics (mwdcs),pseudo phase diagram,developments and challenges,composition-structure-property relationship

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