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RESEARCH PAPER · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2018

The Search for BaTiO3-Based Piezoelectrics With Large Piezoelectric Coefficient Using Machine Learning

Ruihao Yuan, Deqing Xue, Dezhen Xue, Yumei Zhou, Xiangdong Ding, Jun Feng Sun, Turab Lookman

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The Search for BaTiO3-Based Piezoelectrics With Large Piezoelectric Coefficient Using Machine LearningOriginalAccelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design100Bayesian Global Optimization applied to the design of shape-memory alloys6A Critical Review of Machine Learning of Energy Materials606Machine learning assisted multi-objective optimization for materials processing parameters: A case study in Mg alloy119Role of uncertainty estimation in accelerating materials development via active learning45The impact of flexoelectricity on materials, devices, and physics95Knowledge-Based Descriptor for the Compositional Dependence of the Phase Transition in BaTiO3-Based Ferroelectrics22Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys259Identifying Metallic Transition-Metal Dichalcogenides for Hydrogen Evolution through Multilevel High-Throughput Calculations and Machine Learning78Efficient sampling for decision making in materials discovery*10Probing impact of molecular structure on bulk modulus and impact sensitivity of energetic materials by machine learning methods25Disentangling the effect of doping chemistry on the energy storage properties of barium titanate ferroelectrics using data science tools12Optimizing Electrocaloric Effect in Barium Titanate-Based Room Temperature Ferroelectrics: Combining Landau Theory, Machine Learning and Synthesis34Machine learning combined with feature engineering to search for BaTiO3 based ceramics with large piezoelectric constant45Accelerated discovery of high-performance piezocatalyst in BaTiO3-based ceramics via machine learning84Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning44Self-powered sensing systems with learning capability132Machine learning and experiments: A synergy for the development of functional materials19Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning33Optimizing and extending ion dielectric polarizability database for microwave frequencies using machine learning methods55Accelerated Search for KNN-Based Ceramics with Large Piezoelectric Constants Using Machine Learning Methods0Machine Learning Accelerated Discovery of Functional MXenes with Giant Piezoelectric Coefficients41Physics-embedded machine learning search for Sm-doped PMN-PT piezoelectric ceramics with high performance3Engineering strategies to enhance the piezoelectric catalytic performance of inorganic fibers in water treatment and energy regeneration14A systematic review of recent advances in piezocatalysis – Synergetic heterojunctions for organic pollutants removal, immobilization, and scope of machine learning techniques30Multi-iteration active learning for the composition design of potassium–sodium niobate ceramics with enhanced piezoelectric coefficient11Accelerating the prediction of remanent polarization in multicomponent ferroelectrics by using variational autoencoder-based data augmentation8Evading the strength-ductility trade-off dilemma in high-entropy alloys by multi-objective machine learning1Interpretable and uncertainty-informed machine learning to accelerate the design and discovery of lead-free piezoceramics with large piezoelectric constant13Active learning-assisted search for thermal storage used TiNi shape memory alloys1Interpretable Machine Learning for the Design of (K, Na)NbO 3 -Based Piezoceramics Using Combinatorial and Knowledge-Embedded Descriptors3Data-driven multi-process modeling and integrated design strategy for complex high-performance alloys12A review on lead-free piezoelectric ceramic sensors for sustainable and intelligent applications18Exploring quantum active learning for materials design and discovery11Data‐Driven Approach for Simultaneous Optimization of Strength and Conductivity in Al Alloys0Data-driven approaches for functional ceramics3Recent Advances in BaTiO 3 ‐Based Piezocatalysis2Uncertainty-aware multi-source modeling for materials property prediction: A case study in ferroelectrics0Machine learning as a tool for predicting and engineering the next generation of piezoelectric materials0

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  2. Bayesian Global Optimization applied to the design of shape-memory alloys

    Elsevier eBooks · 2020-01-01

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  3. A Critical Review of Machine Learning of Energy Materials

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    Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, Shyue Ping Ong

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  4. Machine learning assisted multi-objective optimization for materials processing parameters: A case study in Mg alloy

    Journal of Alloys and Compounds · 2020-06-23

    Yifei Chen, Yuan Tian, Yumei Zhou, Daqing Fang, Xiangdong Ding, Jun Feng Sun, Dezhen Xue

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  5. Role of uncertainty estimation in accelerating materials development via active learning

    Journal of Applied Physics · 2020-07-02

    Yuan Tian, Ruihao Yuan, Dezhen Xue, Yumei Zhou, Xiangdong Ding, Jun Feng Sun, Turab Lookman

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  6. The impact of flexoelectricity on materials, devices, and physics

    Journal of Applied Physics · 2020-08-25

    Qian Deng, Sihao Lv, Zhaoqi Li, Kai Liang Tan, Liang Xu, Shengping Shen

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  7. Knowledge-Based Descriptor for the Compositional Dependence of the Phase Transition in BaTiO3-Based Ferroelectrics

    ACS Applied Materials & Interfaces · 2020-09-14

    Ruihao Yuan, Deqing Xue, Deqing Xue, Dezhen Xue, Dezhen Xue, Jinshan Li, Xiangdong Ding, Jun Sun, Turab Lookman

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  8. Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys

    Acta Materialia · 2021-01-01

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  9. Identifying Metallic Transition-Metal Dichalcogenides for Hydrogen Evolution through Multilevel High-Throughput Calculations and Machine Learning

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  10. Efficient sampling for decision making in materials discovery*

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  11. Probing impact of molecular structure on bulk modulus and impact sensitivity of energetic materials by machine learning methods

    Chemometrics and Intelligent Laboratory Systems · 2021-04-29

    Qianqian Deng, Jing Hu, Liying Wang, Yijing Liu, Yanzhi Guo, Tao Xu, Xuemei Pu

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  12. Disentangling the effect of doping chemistry on the energy storage properties of barium titanate ferroelectrics using data science tools

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

    Acta Materialia · 2022-01-01

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

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  14. Machine learning combined with feature engineering to search for BaTiO3 based ceramics with large piezoelectric constant

    Journal of Alloys and Compounds · 2022-03-12

    Ruihao Yuan, Deqing Xue, Yangyang Xu, Dezhen Xue, Jinshan Li

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  15. Accelerated discovery of high-performance piezocatalyst in BaTiO3-based ceramics via machine learning

    Nano Energy · 2022-04-04

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  16. Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning

    International Journal of Minerals Metallurgy and Materials · 2022-05-23

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  17. Self-powered sensing systems with learning capability

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  18. Machine learning and experiments: A synergy for the development of functional materials

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

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  20. Optimizing and extending ion dielectric polarizability database for microwave frequencies using machine learning methods

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  21. Accelerated Search for KNN-Based Ceramics with Large Piezoelectric Constants Using Machine Learning Methods

    IEEE International Ultrasonics Symposium · 2023-09-03

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  22. Machine Learning Accelerated Discovery of Functional MXenes with Giant Piezoelectric Coefficients

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  23. Physics-embedded machine learning search for Sm-doped PMN-PT piezoelectric ceramics with high performance

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  24. Engineering strategies to enhance the piezoelectric catalytic performance of inorganic fibers in water treatment and energy regeneration

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