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RESEARCH PAPER · Acta Materialia · 2022

Optimizing Electrocaloric Effect in Barium Titanate-Based Room Temperature Ferroelectrics: Combining Landau Theory, Machine Learning and Synthesis

Ruihao Yuan, Zhen Liu, Yangyang Xu, Ruowei Yin, Jingjin He, Yang Bai, Yumei Zhou, Jinshan Li, Dezhen Xue, Turab Lookman

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42 direct citing works

Optimizing Electrocaloric Effect in Barium Titanate-Based Room Temperature Ferroelectrics: Combining Landau Theory, Machine Learning and SynthesisOriginalA new strategy for long-term complex oxidation of MAX phases: Database generation and oxidation kinetic model establishment with aid of machine learning80Dielectric constant prediction of perovskite microwave dielectric ceramics via machine learning24Order-degree-modulated ferroic response and an unconventional electrocaloric effect in B-site complex systems: a case study in Pb[(Yb1/2Nb1/2)0.84(Mg1/3Nb2/3)0.16]O3 ceramic7Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning29Successive phase transitions–induced multiple boundaries and unprecedented electrocaloric effect in Pb(Yb 1/2 Nb 1/2 )O 3 –Pb(Mg 1/3 Nb 2/3 )O 3 system9Dysprosium doping induced effects on structural, dielectric, energy storage density, and electro-caloric response of lead-free ferroelectric barium titanate ceramics6Pretraining on Matter: Conceptual Limits of Foundation Models for Materials Science0Electrocaloric and energy storage properties of Pb-free 1-x(0.6Ba(Zr0.2Ti0.8)O3 - 0.4(Ba0.7Ca0.3)TiO3)-x(BiTa0.5La0.5)O3 relaxor ferroelectric ceramics6Phase-field simulation and machine learning of low-field magneto-elastocaloric effect in a multiferroic composite15Effect of copper oxide doping on electrocaloric and energy storage properties of lead free ferroelectric 0.6Ba(Zr0.2Ti0.8)O3-0.4(Ba0.7Ca0.3)TiO3 ceramics3Wide working temperature range and large electrocaloric effect in BaTiO3 based ceramics achieved by regulating phase boundaries through a compensatory ion co-doping strategy16AI for dielectric capacitors16Machine learning-assisted accelerated research on piezoelectric response prediction of KNN-based ceramics27Synergy of Oxygen Octahedra Distortion and Polar Nanodomains Induced Emergent Electrocaloric Effect in NaNbO3-Based Ceramics16Unsupervised learning-aided extrapolation for accelerated design of superalloys21Noise‐Aware Active Learning to Develop High‐Temperature Shape Memory Alloys with Large Latent Heat13Machine learning assisted innovative strategy for experimental validation of functional properties of lead-free ceramics8Large electrocaloric effect by disorder regulated structure in BaTiO3-based system3Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks5Strategic optimization of electrocaloric cooling: from material design to device innovation12Machine learning-enabled design of ferroelectrics with multiple properties via a Landau model13Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials68BaTiO3-Based Electrocaloric Materials—Recent Progresses and Perspective10Electrocaloric Effect on Lead-Free Ferroelectrics: Challenges in Identifying Trends and Evaluating Predictive Models5Synergistic multi-ion doping in BaTiO₃-based ceramics: Achieving high electrocaloric efficiency and wide temperature span for miniaturized cooling application6Reverse Design of Electrocaloric Refrigeration: from Cooling Scenarios and Device Architectures to Tailored Materials2Uncertainty estimation affects predictor selection and its calibration improves materials optimization0Lattice entropy change enhanced electrocaloric effect in Ba1-xCexTiO3 system0Superior energy storage performance via engineering crossover region with competing orders in high-entropy multilayer capacitors4Data-driven approaches for functional ceramics3Mapping the electrocaloric landscape of BaTiO3-based systems: a review0Uncertainty-aware multi-source modeling for materials property prediction: A case study in ferroelectrics0Fusing Direct and Indirect Measurements Through Multi‐Fidelity Learning For Accelerated Electrocaloric Materials Discovery0

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  2. Dielectric constant prediction of perovskite microwave dielectric ceramics via machine learning

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  3. Order-degree-modulated ferroic response and an unconventional electrocaloric effect in B-site complex systems: a case study in Pb[(Yb1/2Nb1/2)0.84(Mg1/3Nb2/3)0.16]O3 ceramic

    Journal of Physics D Applied Physics · 2023-06-27

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  4. Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning

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  5. Successive phase transitions–induced multiple boundaries and unprecedented electrocaloric effect in Pb(Yb 1/2 Nb 1/2 )O 3 –Pb(Mg 1/3 Nb 2/3 )O 3 system

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    Feng Li, Mingsheng Long, Daniel Qi Tan, Xiaojie Lou, Lei Shan, Chunchang Wang

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  6. Dysprosium doping induced effects on structural, dielectric, energy storage density, and electro-caloric response of lead-free ferroelectric barium titanate ceramics

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    Muhassinah Tasneem, Koppole Kamakshi

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  7. Pretraining on Matter: Conceptual Limits of Foundation Models for Materials Science

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  8. Electrocaloric and energy storage properties of Pb-free 1-x(0.6Ba(Zr0.2Ti0.8)O3 - 0.4(Ba0.7Ca0.3)TiO3)-x(BiTa0.5La0.5)O3 relaxor ferroelectric ceramics

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  9. Phase-field simulation and machine learning of low-field magneto-elastocaloric effect in a multiferroic composite

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  10. Effect of copper oxide doping on electrocaloric and energy storage properties of lead free ferroelectric 0.6Ba(Zr0.2Ti0.8)O3-0.4(Ba0.7Ca0.3)TiO3 ceramics

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  12. AI for dielectric capacitors

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  14. Synergy of Oxygen Octahedra Distortion and Polar Nanodomains Induced Emergent Electrocaloric Effect in NaNbO3-Based Ceramics

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  15. Unsupervised learning-aided extrapolation for accelerated design of superalloys

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  16. Noise‐Aware Active Learning to Develop High‐Temperature Shape Memory Alloys with Large Latent Heat

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  17. Machine learning assisted innovative strategy for experimental validation of functional properties of lead-free ceramics

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  18. Large electrocaloric effect by disorder regulated structure in BaTiO3-based system

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  19. Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks

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  20. Strategic optimization of electrocaloric cooling: from material design to device innovation

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  21. Machine learning-enabled design of ferroelectrics with multiple properties via a Landau model

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  22. Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials

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  23. BaTiO3-Based Electrocaloric Materials—Recent Progresses and Perspective

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  24. Electrocaloric Effect on Lead-Free Ferroelectrics: Challenges in Identifying Trends and Evaluating Predictive Models

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  25. Synergistic multi-ion doping in BaTiO₃-based ceramics: Achieving high electrocaloric efficiency and wide temperature span for miniaturized cooling application

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  26. Reverse Design of Electrocaloric Refrigeration: from Cooling Scenarios and Device Architectures to Tailored Materials

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  27. Uncertainty estimation affects predictor selection and its calibration improves materials optimization

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  28. Lattice entropy change enhanced electrocaloric effect in Ba1-xCexTiO3 system

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  33. Fusing Direct and Indirect Measurements Through Multi‐Fidelity Learning For Accelerated Electrocaloric Materials Discovery

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