Dielectric ceramics are the basic building block of every electronic device. There is an increasing demand from a range of industries including telecommunication, medical, automobile, aerospace applications for the development of IoT, sensors, actuators, antennas, filters, and baluns. The material properties such as dielectric constant, dielectric loss, coefficient of thermal expansion, thermal conductivities, flexural strength, young's modulus play a key role in the specific application for manufacturing devices. Polar dielectric materials like piezoelectric, ferroelectric, and multiferroics have triggered vibrant fundamental studies in computational physical chemistry and experimental materials engineering. They are also leading the way towards advanced electronic components including transistors, memristors, and transducers. Very recently, Artificial Intelligence-driven materials discovery is a radical solution to this problem that leverages advanced deep learning and machine learning techniques in extracting insights from materials literature.
Due to recent advancements in energy regulation, new research developments are gaining significant attention in materials science. For example, ultra-low temperature co-fired ceramics (ULTCC), superconductor electronics for quantum computing (SCE), Cold sintered ceramics (CSC) and room temperature fabrications are getting more and more attractions in this scenario. The recent development in printed electronics from 2.5 D to 3D and 4D printing of intelligent ceramics for next-generation electronic modules. In addition, sustainability and circular economic developments are the upcoming strategies for the dielectric research world. Computational studies have tried to simulate a limited number of parameters, mainly bandgap and polarization at the current stage. Other useful factors including conductivity, permittivity, piezoelectricity, and magnetization need to be considered together with the manipulation of band gaps in further research. In addition to traditional computation, AI (artificial intelligence)-assisted methods can be introduced to accelerate the search for viable material structures. The goal of the AI-driven materials discovery and development initiative is to:
(1) Accelerate the discovery of new materials and their properties
(2) Extract useful insights from literature
(3) Autonomous design of experiments and
(4) High throughput experimentation.
Several recent discoveries such as the development of new piezoelectrics and thermoelectric are testaments to the power of this approach. Areas of interest for this new Research Topic could include, but are not limited to:
• UHTCC, HTCC, LTCC, ULTCC materials, and devices.
• 2.5D, 3D, and 4D printed dielectrics.
• Microwave/mmWave materials.
• Fabrication and characterization of narrow bandgap piezoelectrics, ferroelectric, and multiferroics.
• Micro- or nano-insights of photo-induced electro-/mechano-/thermo- striction, ferroelectricity, permittivity, conduction, and piezoelectricity.
• Original discoveries or development of new materials through AI/ML approaches.
• AI/ML-driven improvements in scientific instrumentation for materials characterization.
• Developments in the design of experiments with demonstrable results.
Dielectric ceramics are the basic building block of every electronic device. There is an increasing demand from a range of industries including telecommunication, medical, automobile, aerospace applications for the development of IoT, sensors, actuators, antennas, filters, and baluns. The material properties such as dielectric constant, dielectric loss, coefficient of thermal expansion, thermal conductivities, flexural strength, young's modulus play a key role in the specific application for manufacturing devices. Polar dielectric materials like piezoelectric, ferroelectric, and multiferroics have triggered vibrant fundamental studies in computational physical chemistry and experimental materials engineering. They are also leading the way towards advanced electronic components including transistors, memristors, and transducers. Very recently, Artificial Intelligence-driven materials discovery is a radical solution to this problem that leverages advanced deep learning and machine learning techniques in extracting insights from materials literature.
Due to recent advancements in energy regulation, new research developments are gaining significant attention in materials science. For example, ultra-low temperature co-fired ceramics (ULTCC), superconductor electronics for quantum computing (SCE), Cold sintered ceramics (CSC) and room temperature fabrications are getting more and more attractions in this scenario. The recent development in printed electronics from 2.5 D to 3D and 4D printing of intelligent ceramics for next-generation electronic modules. In addition, sustainability and circular economic developments are the upcoming strategies for the dielectric research world. Computational studies have tried to simulate a limited number of parameters, mainly bandgap and polarization at the current stage. Other useful factors including conductivity, permittivity, piezoelectricity, and magnetization need to be considered together with the manipulation of band gaps in further research. In addition to traditional computation, AI (artificial intelligence)-assisted methods can be introduced to accelerate the search for viable material structures. The goal of the AI-driven materials discovery and development initiative is to:
(1) Accelerate the discovery of new materials and their properties
(2) Extract useful insights from literature
(3) Autonomous design of experiments and
(4) High throughput experimentation.
Several recent discoveries such as the development of new piezoelectrics and thermoelectric are testaments to the power of this approach. Areas of interest for this new Research Topic could include, but are not limited to:
• UHTCC, HTCC, LTCC, ULTCC materials, and devices.
• 2.5D, 3D, and 4D printed dielectrics.
• Microwave/mmWave materials.
• Fabrication and characterization of narrow bandgap piezoelectrics, ferroelectric, and multiferroics.
• Micro- or nano-insights of photo-induced electro-/mechano-/thermo- striction, ferroelectricity, permittivity, conduction, and piezoelectricity.
• Original discoveries or development of new materials through AI/ML approaches.
• AI/ML-driven improvements in scientific instrumentation for materials characterization.
• Developments in the design of experiments with demonstrable results.
