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RESEARCH PAPER · Advanced Science · 2019

Accelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design

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

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Accelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental DesignOriginalPrediction and optimization of epoxy adhesive strength from a small dataset through active learning102Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research104Multiscale structural engineering of dielectric ceramics for energy storage applications: from bulk to thin films253Bi-directional prediction of structural characteristics and effective thermal conductivities of composite fuels through learning from finite element simulation results23Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges191Microfluidic Synthesis of Luminescent and Plasmonic Nanoparticles: Fast, Efficient, and Data‐Rich87Machine learning assisted multi-objective optimization for materials processing parameters: A case study in Mg alloy119Role of uncertainty estimation in accelerating materials development via active learning45Knowledge-Based Descriptor for the Compositional Dependence of the Phase Transition in BaTiO3-Based Ferroelectrics22Integrating data mining and machine learning to discover high-strength ductile titanium alloys190Determining Multi‐Component Phase Diagrams with Desired Characteristics Using Active Learning49Strategies to Improve the Energy Storage Properties of Perovskite Lead-Free Relaxor Ferroelectrics: A Review191Phase stability and Landau phenomenological model of relaxor ferroelectric single crystals 0.78Pb(Mg1/3Nb2/3)O3-0.22PbTiO38Phase diagram and dielectric properties of orientation-dependent PbZr0.52Ti0.48O3 epitaxial films0Efficient estimation of material property curves and surfaces via active learning24Rational design of nanomaterials for high energy density dielectric capacitors via electrospinning66Enhanced Energy-Storage Density by Reversible Domain Switching in Acceptor-Doped Ferroelectrics9Machine-Learning-Enabled Prediction of Adiabatic Temperature Change in Lead-Free BaTiO3-Based Electrocaloric Ceramics20Design of Experiments and machine learning for product innovation: A systematic literature review95Local structure engineered lead-free ferroic dielectrics for superior energy-storage capacitors: A review324Combined merits of high dielectric breakdown strength and low sintering temperature acquired in MgO-based dielectric ceramics: from theoretical prediction to experimental validation8Disentangling the effect of doping chemistry on the energy storage properties of barium titanate ferroelectrics using data science tools12Estimating the performance of a material in its service space via Bayesian active learning: a case study of the damping capacity of Mg alloys14Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models11Optimizing Electrocaloric Effect in Barium Titanate-Based Room Temperature Ferroelectrics: Combining Landau Theory, Machine Learning and Synthesis34Global‐Oriented Strategy for Searching Ultrastrength Martensitic Stainless Steels11Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate24Machine learning in energy storage materials106Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials Discovery26High‐Throughput Method–Accelerated Design of Ni‐Based Superalloys61Active learning for noisy physical experiments with more than two responses2Hyper-Ordered Structures in Dielectric Materials5Descriptor Aided Bayesian Optimization for Many-Level Qualitative Variables With Materials Design Applications11Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys62Accelerated discovery of cost-effective Nd–Fe–B magnets through adaptive learning7Machine learning-assisted design of AlN-based high-performance piezoelectric materials17Machine learning and experiments: A synergy for the development of functional materials19Active learning for optimum experimental design—insight into perovskite oxides8Data quantity governance for machine learning in materials science228Improved energy storage performance of lead-free BaTi0.96Li0.04O2.94 ceramics via domain structure engineering29Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics27Machine learning aided accelerated prediction and experimental validation of functional properties of K1-xNaxNbO3-based piezoelectric ceramics18Multi‐objective optimization and its application in materials science46Efficient property-oriented optimization of magnetic high-entropy metallic glasses via a multi-stage design strategy5An interpretable machine learning strategy for pursuing high piezoelectric coefficients in (K0.5Na0.5)NbO3-based ceramics52ASSESSMENT OF DATA COLLECTION TECHNIQUES IN MANUFACTURING AND MECHANICAL ENGINEERING THROUGH MACHINE LEARNING MODELS41Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective60Physics-embedded machine learning search for Sm-doped PMN-PT piezoelectric ceramics with high performance3AI for dielectric capacitors16Accelerated learning and co‐optimization of elastocaloric effect and stress hysteresis of elastocaloric alloys10Recent progress on machine learning with limited materials data: Using tools from data science and domain knowledge31Review of the opportunities and limitations for powder-based high-throughput solid-state processing of advanced functional ceramics36Unsupervised learning-aided extrapolation for accelerated design of superalloys21Enhancing energy storage performance in barium titanate ceramics through mg-doping via creation of defect dipoles engineering5Prediction of thermal conductivity of paraffin-expanded graphite composite phase change materials by machine learning method16Machine learning assisted innovative strategy for experimental validation of functional properties of lead-free ceramics8Machine-Learning-Driven Predictive 3D Ramified Foam Fabrication and Mechanistic Understanding1Machine learning-assisted design of CaBi2Nb2O9-based ceramics with high piezoelectricity and thermal stability8Accelerated design of AgNbO 3 -based ceramics with high energy storage performance via machine learning8AI4Materials: Transforming the landscape of materials science and enigneering25Cuckoo 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 constant13Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration36Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty21Construction of material phase diagram using different uncertainty estimation strategies2Enhanced energy storage in relaxor (1-x)Bi0.5Na0.5TiO3-xBaZryTi1-yO3 thin films by morphotropic phase boundary engineering17Machine learning-enabled design of ferroelectrics with multiple properties via a Landau model13Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage52Weakly‐coupled barium titanate stannate‐based relaxors as energy storage materials8Machine Learning-Assisted Accelerated Research of Energy Storage Properties of BaTiO3–BiMeO3 Ceramics10Unveiling the Origin of Ultrahigh Piezoelectricity in Sb Doped KNN Based Piezoceramics23Machine learning accelerates composition design of lead-free high performance piezoceramics based on potassium sodium niobate5Ultra-high strain under low driving field via synergetic design in high-entropy relaxor ceramics7Machine Learning Enhanced Self‐Charging Power Sources21Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials68Designing Pb-Free High-Entropy Relaxor Ferroelectrics with Machine Learning Assistance for High Energy Storage38Machine learning assisted electrocaloric and pyroelectric performance of Ba0.85Ca0.15Ti0.9Zr0.1O3 ceramic for solid state refrigeration4Machine Learning‐Driven Ultra‐High Energy Storage Performance in Ferro‐Superparaelectric Capacitors29AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management31Sustainable Materials for Energy9Optimized energy storage performance in HZO3ZO12 thin films through modulation of deposition temperature and film thickness2Modeling of ferroelectric properties of KNN-based ceramics1Data-driven and machine learning-assisted research on ferroelectric materials0Exploring quantum active learning for materials design and discovery11Interpretable machine learning-assisted discovery of BaTiO 3 -based ceramics with enhanced dielectric constant1Uncertainty estimation affects predictor selection and its calibration improves materials optimization0Machine-learning-accelerated design of superparaelectric capacitors for ultrahigh energy storage1Machine-learning for capacitive energy storage polymer dielectrics: opportunities and challenges9Machine learning-driven BaTiO 3 -based high-entropy ceramics with ultrahigh energy storage density from crossover region9Machine Learning‐Assisted Design of BaTiO 3 ‐Based Superparaelectric High‐Entropy Ceramics with Superior Energy Storage0Active learning in latent spaces enables rapid inverse design of ferroelectric ceramics for energy storage2Artificial intelligence-driven methods for energy storage innovation and intelligent management2Establishing a Trustworthy Discovery Workflow for High-Entropy BNT Ceramics via SHAP-Interpretable Machine Learning0Data-driven approaches for functional ceramics3Atomic‐Scale Intermediate Polarization States Enable Superb Energy Storage in NaNbO 3 Ceramics via Machine Learning5La3+ doping-induced superparaelectric state for superior energy storage in Bi0.5Na0.5TiO3-Based ceramics under medium electric fields0Ferroelectric tunable dielectric Materials: Response mechanisms and applications0Uncertainty-aware multi-source modeling for materials property prediction: A case study in ferroelectrics0Site-selective Ca2+ doping in BaTiO3: Defect chemistry, phase stability, functional response, and machine-learning-assisted design0

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