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RESEARCH PAPER · Journal of Advanced Ceramics · 2023

Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning

Wei Gu, Bin Yang, Dengfeng Li, Xunzhong Shang, Zhiyong Zhou, Jinming Guo

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Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learningOriginalMachine learning aided accelerated prediction and experimental validation of functional properties of K1-xNaxNbO3-based piezoelectric ceramics18Materials Development and Potential Applications of Ceramics: New Opportunities and Challenges5Acceleration of optimizing the dielectric properties of (Ca 0.7 Nd 0.2 ) 1− x (Li 0.5 Nd 0.5 ) x Ti 1− y (Mg 1/3 Nb 2/3 ) y O 3 with the aid of machine learning9Autoencoded chemical feature interaction machine learning method boosting performance of piezoelectric catalytic process16Detection and classification of SMC composites based on LIBS1Intelligent prediction of Si3N4 ceramic strength considering inherent defect characteristics12Machine learning assisted innovative strategy for experimental validation of functional properties of lead-free ceramics8Multi-iteration active learning for the composition design of potassium–sodium niobate ceramics with enhanced piezoelectric coefficient11An interpretable machine learning model for Mn-based cathode development: mapping synthesis parameters to cycling stability6Cuckoo Search-Deep Neural Network Hybrid Model for Uncertainty Quantification and Optimization of Dielectric Energy Storage in Na1/2Bi1/2TiO3-Based Ceramic Capacitors0Interpretable and uncertainty-informed machine learning to accelerate the design and discovery of lead-free piezoceramics with large piezoelectric constant13Machine Learning Guided Design and Ablation Behavior of Zrc-Tac-Sic Ternary Coatings2Machine Learning Guided Design and Ablation Behavior of Zrc-Tac-Sic Ternary Coatings0Exceptionally optimized millimeter-wave properties of cordierite-based materials via innovative processing and predictive analytics7Machine Learning-Assisted Accelerated Research of Energy Storage Properties of BaTiO3–BiMeO3 Ceramics10Rapid detection and classification of PC + ABS meter box materials based on laser-induced breakdown spectroscopy0Machine Learning-Assisted Catalysts for Advanced Oxidation Processes: Progress, Challenges, and Prospects35Synergistically enhancing the electrical resistivity and thermal stability of Bi4Ti3O12 ceramics by multiple-ions doping of Ce/W/Mo9Machine learning assisted prediction for the coefficient of thermal expansion of binary crystals26Nitrogen-induced stacking fault energy reduction enables spinodal-decomposition-driven toughening in (Ti,Zr)C ceramics9Interpretable Machine Learning for the Design of (K, Na)NbO 3 -Based Piezoceramics Using Combinatorial and Knowledge-Embedded Descriptors3Data-driven framework for accelerated design of cerium alloy surrogates for uranium hydriding behavior0Synergistic optimization of structures and properties in NBT ceramics via heterovalent ion substitution at B‐site3A review on lead-free piezoelectric ceramic sensors for sustainable and intelligent applications18Interpretable machine learning-assisted discovery of BaTiO 3 -based ceramics with enhanced dielectric constant1Leakage-Controlled High-( d 33 ) Prediction and Uncertainty-Aware Candidate Prioritization in Lead-Free KNN-Based Piezoelectric Ceramics Using PGU-RNet0Recent advances in flexible piezoelectric composite thin films: Material innovations and performance optimization2Accelerated Discovery of Multifunctional K 0.5 Na 0.5 NbO 3 ‐Based Ceramics via Integrated High‐Throughput Computation and Machine Learning5Li/Mn co-doped K 0.5 Bi 4.5 Ti 4 O 15 high-temperature piezoelectric ceramics: Structures, properties, and ultrasonic transducer applications12Data-driven approaches for functional ceramics3Advancement of nano-based piezoelectric material with integration of artificial intelligence in aerospace1Reliable and interpretable crystalline-phase prediction framework for MAS glass ceramics with experimental validation0Machine learning as a tool for predicting and engineering the next generation of piezoelectric materials0

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  2. Materials Development and Potential Applications of Ceramics: New Opportunities and Challenges

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  4. Autoencoded chemical feature interaction machine learning method boosting performance of piezoelectric catalytic process

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  5. Detection and classification of SMC composites based on LIBS

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  6. Intelligent prediction of Si3N4 ceramic strength considering inherent defect characteristics

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  8. Multi-iteration active learning for the composition design of potassium–sodium niobate ceramics with enhanced piezoelectric coefficient

    Ceramics International · 2024-10-22

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  11. Interpretable and uncertainty-informed machine learning to accelerate the design and discovery of lead-free piezoceramics with large piezoelectric constant

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  14. Exceptionally optimized millimeter-wave properties of cordierite-based materials via innovative processing and predictive analytics

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  15. Machine Learning-Assisted Accelerated Research of Energy Storage Properties of BaTiO3–BiMeO3 Ceramics

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  16. Rapid detection and classification of PC + ABS meter box materials based on laser-induced breakdown spectroscopy

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  20. Nitrogen-induced stacking fault energy reduction enables spinodal-decomposition-driven toughening in (Ti,Zr)C ceramics

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  21. Interpretable Machine Learning for the Design of (K, Na)NbO 3 -Based Piezoceramics Using Combinatorial and Knowledge-Embedded Descriptors

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  22. Data-driven framework for accelerated design of cerium alloy surrogates for uranium hydriding behavior

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  24. A review on lead-free piezoelectric ceramic sensors for sustainable and intelligent applications

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