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RESEARCH PAPER · Advanced Science · 2020

Determining Multi‐Component Phase Diagrams with Desired Characteristics Using Active Learning

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

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42 direct citing works

Determining Multi‐Component Phase Diagrams with Desired Characteristics Using Active LearningOriginalA Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications50Efficient estimation of material property curves and surfaces via active learning24Machine-Learning-Enabled Prediction of Adiabatic Temperature Change in Lead-Free BaTiO3-Based Electrocaloric Ceramics20Decoding hexanitrobenzene (HNB) and 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as two distinctive energetic nitrobenzene compounds by machine learning24Disentangling 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 Assisted Predictions of Multi-Component Phase Diagrams and Fine Boundary Information56Optimizing Electrocaloric Effect in Barium Titanate-Based Room Temperature Ferroelectrics: Combining Landau Theory, Machine Learning and Synthesis34Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning32Using Feature‐Assisted Machine Learning Algorithms to Boost Polarity in Lead‐Free Multicomponent Niobate Alloys for High‐Performance Ferroelectrics37Machine-Learning-Based phase diagram construction for high-throughput batch experiments15Evaluation guidelines for machine learning tools in the chemical sciences180Recent progress toward catalyst properties, performance, and prediction with data-driven methods17XGBoost model for electrocaloric temperature change prediction in ceramics70Machine learning accelerates the materials discovery142Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Materials property prediction with uncertainty quantification: A benchmark study41Estimating the temperature dependent zero-phase-fraction features in ternary phase diagram via Bayesian approach6Recent applications of machine learning in alloy design: A review158Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics27Machine Learning for the Optimization of Chemical Reaction Conditions6Multi‐objective optimization and its application in materials science46Compositional design of multicomponent alloys using reinforcement learning45Effect of Cu content on martensitic transformation and shape memory behavior in Ti31.5Hf15Zr5Ni48.5−xCux alloys4Accelerating the Exploration of High‐Entropy Alloys: Synergistic Effects of Integrating Computational Simulation and Experiments40Compositional design of compounds with elements not in training data using supervised learning1Targeted materials discovery using Bayesian algorithm execution48Review of the opportunities and limitations for powder-based high-throughput solid-state processing of advanced functional ceramics36AIPHAD, an active learning web application for visual understanding of phase diagrams12Noise‐Aware Active Learning to Develop High‐Temperature Shape Memory Alloys with Large Latent Heat13Multi-objective design of Ni-B-Al master alloy by adaptive machine learning-driven aluminothermic reduction experiment3Black-box optimization technique for investigation of surface phase diagram0Data efficiency of classification strategies for chemical and materials design5AI4Materials: Transforming the landscape of materials science and enigneering25Leveraging feature gradient for efficient acquisition function maximization in material composition design3Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty21Accelerated design of a novel wide thermal hysteresis NiTi-based shape memory alloy based on interpretable information machine learning12Design of corrosion-resistant eutectic high-entropy alloys via hybrid data-driven and expert-guided strategies17Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning16The synergy of machine learning and CALPHAD: Revitalizing traditional approaches16Materials design with target-oriented Bayesian optimization40Bayesian diversity control for batch-based phase diagram determination0Reverse Design of Electrocaloric Refrigeration: from Cooling Scenarios and Device Architectures to Tailored Materials2Uncertainty estimation affects predictor selection and its calibration improves materials optimization0Application of green dissolution separation and recycling of waste polyester/cotton/polyurethane blended fabric1Physics-embedded unsupervised adaptive classification of Lennard-Jones–Gauss potentials0Artificial intelligence for advanced alloy: multiscale simulation, intelligent manufacturing, high-throughput characterization, and service performance intelligent assessment0Multi-activity derivative automated labeling and margin-sampling active learning for efficient determination of phase boundaries0

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  3. Machine-Learning-Enabled Prediction of Adiabatic Temperature Change in Lead-Free BaTiO3-Based Electrocaloric Ceramics

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

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  9. Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning

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  10. Using Feature‐Assisted Machine Learning Algorithms to Boost Polarity in Lead‐Free Multicomponent Niobate Alloys for High‐Performance Ferroelectrics

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