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RESEARCH PAPER · Advanced Materials · 2018

Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning

Ruihao Yuan, Zhen Liu, Prasanna V. Balachandran, Deqing Xue, Deqing Xue, Yumei Zhou, Xiangdong Ding, Jun Sun, Dezhen Xue, Dezhen Xue, Turab Lookman

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Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active LearningOriginalAn artificial intelligence atomic force microscope enabled by machine learning96Doping Effects of Point Defects in Shape Memory Alloys33Matminer: An open source toolkit for materials data mining1069Electromechanical response and piezoelectric properties in (Ba0.85Ca0.15)(Zr0.1Ti0.9)O3 piezoceramics using nano-sized AlN modification23Origin of large electrostrain in Sn4+ doped Ba(Zr0.2Ti0.8)O3-x(Ba0.7Ca0.3)TiO3 ceramics36Investigation into the topology optimization for conductive heat transfer based on deep learning approach131Fluorescence Color by Data-Driven Design of Genomic Silver Clusters82A machine learning approach for engineering bulk metallic glass alloys246Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning722Mapping intrinsic electromechanical responses at the nanoscale via sequential excitation scanning probe microscopy empowered by deep data34Artificial neural network enabled capacitance prediction for carbon-based supercapacitors137Efficient Optimization of the Performance of Mn2+‐Doped Kesterite Solar Cell: Machine Learning Aided Synthesis of High Efficient Cu2(Mn,Zn)Sn(S,Se)4 Solar Cells58BiFeO3-BaTiO3: A new generation of lead-free electroceramics237The Use of Deep Learning to Fast Evaluate Organic Photovoltaic Materials72Structure evolution and ferroelectric properties in stoichiometric Bi0.5+xNa0.5−xTi1−0.5xO310The Search for BaTiO3-Based Piezoelectrics With Large Piezoelectric Coefficient Using Machine Learning39Machine learning in materials design and discovery: Examples from the present and suggestions for the future198A quantitative uncertainty metric controls error in neural network-driven chemical discovery240Enhanced Piezoelectric Properties and Temperature Stability of 0.5BZT-0.5BCT Ceramic Induced by Using Three-Step Synthesizing Method1High-throughput sequential excitation for nanoscale mapping of electrochemical strain in granular ceria13Machine learning for renewable energy materials312Superior and anti-fatigue electro-strain in Bi 0.5 Na 0.5 TiO 3 -based polycrystalline relaxor ferroelectrics68Abrupt Structural Transformation in Asymmetric ABPO4F (A = K, Rb, Cs)21Materials informatics: From the atomic-level to the continuum191Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design832Machine learning assisted design of high entropy alloys with desired property936Machine learning guided design of functional materials with targeted properties60A two-stage predicting model for γ′ solvus temperature of L12-strengthened Co-base superalloys based on machine learning49Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learning13Improving the performance of lead-free piezoelectric composites by using polycrystalline inclusions and tuning the dielectric matrix environment34COSMO-based solvent selection and Aspen Plus process simulation for tar absorptive removal17Bayesian optimization of chemical composition: A comprehensive framework and its application to RFe12 -type magnet compounds46Research on ancient ceramic identification by artificial intelligence36Active-learning and materials design: the example of high glass transition temperature polymers143Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning116Enumeration of de novo inorganic complexes for chemical discovery and machine learning42Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics185Embedding Physics Domain Knowledge into a Bayesian Network Enables Layer-by-Layer Process Innovation for Photovoltaics4Accelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design100Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress317Inverse Design of Solid-State Materials via a Continuous Representation379Prediction and optimization of epoxy adhesive strength from a small dataset through active learning102Large strain with low hysteresis in Sn-modified Bi0.5(Na0.75K0.25)0.5TiO3 lead-free piezoceramics28Machine Learning for Accelerated Discovery of Solar Photocatalysts179Method for directly and instantaneously predicting conductive heat transfer topologies by using supervised deep learning24Unsupervised discovery of solid-state lithium ion conductors312Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models461Accelerated discovery of potential ferroelectric perovskiteviaactive learning35Bayesian Global Optimization applied to the design of shape-memory alloys6Benchmarking the acceleration of materials discovery by sequential learning149Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening310Learning with Large Databases0Learning with Small Databases0Defects and Aliovalent Doping Engineering in Electroceramics353Accelerated design of novel W-free high-strength Co-base superalloys with extremely wide γ/γʹ region by machine learning and CALPHAD methods98Bi-directional prediction of structural characteristics and effective thermal conductivities of composite fuels through learning from finite element simulation results23Growing field of materials informatics: databases and artificial intelligence22A Critical Review of Machine Learning of Energy Materials606Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics46Artificial Intelligence to Power the Future of Materials Science and Engineering154Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods10Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization198Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation103Band-Gap Engineering of Mo- and W-Containing Perovskite Oxides Derived from Barium Titanate7Development of use-specific high-performance cyber-nanomaterial optical detectors by effective choice of machine learning algorithms12Machine learning in materials genome initiative: A review231Opportunities and Challenges for Machine Learning in Materials Science523Band Structure Engineering in MoS2 Based Heterostructures toward High‐Performance Phototransistors56Machine learning assisted design of γ′-strengthened Co-base superalloys with multi-performance optimization120Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials88Improved strain and low hysteresis in (0.9-x)BaTiO3-xCaTiO3-0.1Ba(Zr0.7Sn0.3)O3 lead-free relaxor ferroelectrics7Optimization of thermal conductivity of UO2–Mo composite with continuous Mo channel based on finite element method and machine learning25Machine learning assisted multi-objective optimization for materials processing parameters: A case study in Mg alloy119Optimization of Material Composition of Li‐Intercalated Metal–Organic Framework Electrodes Using a Combination of Experiments and Machine Learning of X‐Ray Diffraction Patterns11Ultrahigh piezoelectricity in lead-free piezoceramics by synergistic design172Accelerated Discovery of Novel Inorganic Materials with Desired Properties Using Active Learning20Machine learning assisted materials design and discovery for rechargeable batteries466Metal-Free Hybrid Organic–Inorganic Perovskites for Photovoltaics19Role of uncertainty estimation in accelerating materials development via active learning45Machine 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learning105Machine learning in materials design: Algorithm and application*62Integrating data mining and machine learning to discover high-strength ductile titanium alloys190Emerging materials intelligence ecosystems propelled by machine learning441Determining Multi‐Component Phase Diagrams with Desired Characteristics Using Active Learning49On-the-fly closed-loop materials discovery via Bayesian active learning542Navigating the Complex Compositional Landscape of High-Entropy Alloys2Accelerated discovery of stable spinels in energy systems via machine learning56Deep Mining Stable and Nontoxic Hybrid Organic–Inorganic Perovskites for Photovoltaics via Progressive Machine Learning46Strategies to Improve the Energy Storage Properties of Perovskite Lead-Free Relaxor Ferroelectrics: A Review191Artificial intelligence and machine learning in design of mechanical materials690Investigating Active Learning and Meta-Learning for Iterative Peptide Design34Accelerated discovery of boron-dipyrromethene sensitizer for solar cells by integrating data mining and first principle21A property-oriented adaptive design framework for rapid discovery of energetic molecules based on small-scale labeled datasets15Accelerated design of promising mixed lead-free double halide organic–inorganic perovskites for photovoltaics using machine learning44Harnessing the Complex Compositional Space of High-Entropy Alloys9High-throughput design of magnetic materials3Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys259Machine learning-guided design and development of metallic structural materials27Materials Informatics Approach to Predictive Models for Elastic Modulus of Polypropylene Composites Reinforced by Fillers and Additives2Efficient estimation of material property curves and surfaces via active learning24Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design55Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning50Perspective—Accelerated Discovery of Organic-Inorganic Hybrid Materials via Machine Learning1Machine learning identified materials descriptors for ferroelectricity110Efficient sampling for decision making in materials discovery*10Most frequently asked questions about the coercivity of Nd-Fe-B permanent magnets169The Prediction of the Undercooling Degree of As-Cast Irons and Aluminum Alloys via Machine Learning7The learning of the precipitates morphological parameters from the composition of nickel-based superalloys20Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors36Machine-learning micropattern manufacturing45Probing impact of molecular structure on bulk modulus and impact sensitivity of energetic materials by machine learning methods25Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints31A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries82Bayesian optimization for goal-oriented multi-objective inverse material design36Photocatalytic overall water splitting by graphitic carbon nitride140Discovery of aluminum alloys with ultra-strength and high-toughness via a property-oriented design strategy170Catalyze Materials Science with Machine Learning67Polymer‐/Ceramic‐based Dielectric Composites for Energy Storage and Conversion201Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Active-Learning-Based Generative Design for the Discovery of Wide-Band-Gap Materials22Accelerated Discovery of Proton-Conducting Perovskite Oxide by Capturing Physicochemical Fundamentals of Hydration75Machine-Learning-Enabled Tricks of the Trade for Rapid Host Material Discovery in Li–S Battery47Self‐Driving Platform for Metal Nanoparticle Synthesis: Combining Microfluidics and Machine Learning134Large electrostrain with nearly-vanished hysteresis in eco-friendly perovskites by building coexistent glasses near quadruple point38A strategy assisted machine learning to process multi-objective optimization for improving mechanical properties of carbon steels95Artificial intelligence for search and discovery of quantum materials68Machine-Learning-Enabled Prediction of Adiabatic Temperature Change in Lead-Free BaTiO3-Based Electrocaloric Ceramics20Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization77Accurate bandgap predictions of solids assisted by machine learning44Artificial intelligence: A powerful paradigm for scientific research1702A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness335Deformation potential extraction and computationally efficient mobility calculations in silicon from first principles67Bayesian Machine Learning for Efficient Minimization of Defects in ALD Passivation Layers30Material informatics for uranium-bearing equiatomic disordered solid solution alloys4High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research140Composition optimization of radiation resistance ODS alloy with high strength and ductility for advanced reactor based on machine learning8On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high Curie temperature92Discovery of Lead‐Free Perovskites for High‐Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities83Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams98

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