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RESEARCH PAPER · Acta Materialia · 2019

Machine learning assisted design of high entropy alloys with desired property

Cheng Wen, Yan Zhang, Changxin Wang, Dezhen Xue, Yang Bai, Stoichko Antonov, Lan-Hong Dai, Turab Lookman, Yanjing Su

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Machine learning assisted design of high entropy alloys with desired propertyOriginalPhase Stabilities of High Entropy Alloys130Prediction of the Composition and Hardness of High-Entropy Alloys by Machine Learning171Data‐Driven Materials Science: Status, Challenges, and Perspectives700Accelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design100Uncovering the eutectics design by machine learning in the Al–Co–Cr–Fe–Ni high entropy system275A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys285Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models461Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening310High entropy alloy electrocatalysts: a critical assessment of fabrication and performance202Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research104Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges191Machine learning based hierarchy of causative variables for tool failure in friction stir welding68Machine Learning for Structural Materials61Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules221Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches161Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Machine learning assisted design of γ′-strengthened Co-base superalloys with multi-performance optimization120Machine learning reveals the importance of the formation enthalpy and atom-size difference in forming phases of high entropy alloys135A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data61Designing high entropy superalloys for elevated temperature application120Machine learning assisted multi-objective optimization for materials processing parameters: A case study in Mg alloy119Accelerated prediction of Vickers hardness of Co- and Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning78Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus78Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging89Machine learning in additive manufacturing: State-of-the-art and perspectives693Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys93Predicting Tensile Properties of AZ31 Magnesium Alloys by Machine Learning66Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels257Modelling and prediction of hardness in multi-component alloys: A combined machine learning, first principles and experimental study68High-Throughput Calculations for High-Entropy Alloys: A Brief Review135Coupling physics in machine learning to predict properties of high-temperatures alloys95Machine learning-based glass formation prediction in multicomponent alloys133Accelerated discovery of high-strength aluminum alloys by machine learning105Multicomponent high-entropy Cantor alloys877Machine learning-assisted discovery of strong and conductive Cu alloys: Data mining from discarded experiments and physical features86Deep learning-based phase prediction of high-entropy alloys: Optimization, generation, and explanation226Emerging materials intelligence ecosystems propelled by machine learning441Improvement of the machine learning-based corrosion rate prediction model through the optimization of input features202Yield strength prediction of high-entropy alloys using machine learning130The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions69Expanded dataset of mechanical properties and observed phases of multi-principal element alloys136Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses110Machine learning strategies for high-entropy alloys72Artificial intelligence and machine learning in design of mechanical materials690Machine learning-based prediction of phases in high-entropy alloys149High-entropy energy materials: challenges and new opportunities960Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys259Mechanical behavior of high-entropy alloys1321A general and transferable deep learning framework for predicting phase formation in materials93Machine learning guided automatic recognition of crystal boundaries in bainitic/martensitic alloy and relationship between boundary types and ductile-to-brittle transition behavior69Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study133Machine learning of phases and mechanical properties in complex concentrated alloys162Modelling and optimization of microhardness of electroless Ni–P–TiO2 composite coating based on machine learning approaches and RSM82Predictive descriptors in machine learning and data-enabled explorations of high-entropy alloys73Machine learning for design, phase transformation and mechanical properties of alloys121A perspective on corrosion of multi-principal element alloys145Determination of glass forming ability of bulk metallic glasses based on machine learning58Prediction of Mechanical Properties of Wrought Aluminium Alloys Using Feature Engineering Assisted Machine Learning Approach62Rapid discovery of narrow bandgap oxide double perovskites using machine learning59Modeling solid solution strengthening in high entropy alloys using machine learning254A focused review on machine learning aided high-throughput methods in high entropy alloy134Discovery of aluminum alloys with ultra-strength and high-toughness via a property-oriented design strategy170Machine learning elastic constants of multi-component alloys68Machine Learning to Predict Quasicrystals from Chemical Compositions62Machine learning for alloys698Accelerated discovery of single-phase refractory high entropy alloys assisted by machine learning98Machine learning prediction of magnetic properties of Fe-based metallic glasses considering glass forming ability75Biomaterials by design: Harnessing data for future development58Phase–Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries58Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges67Machine Learning‐Driven Biomaterials Evolution299A strategy assisted machine learning to process multi-objective optimization for improving mechanical properties of carbon steels95Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization67Machine learning assisted modelling and design of solid solution hardened high entropy alloys97High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications107A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness335Design of Experiments and machine learning for product innovation: A systematic literature review95High throughput synthesis enabled exploration of CoCrFeNi-based high entropy alloys61Efficient alloy design of Sr-modified A356 alloys driven by computational thermodynamics and machine learning73Crack free metal printing using physics informed machine learning76Machine-learning and high-throughput studies for high-entropy materials165Hardness prediction of high entropy alloys with machine learning and material descriptors selection by improved genetic algorithm97Progress and Perspective of Metallic Glasses for Energy Conversion and Storage71Automated pipeline for superalloy data by text mining83Development and Property Tuning of Refractory High-Entropy Alloys: A Review98Machine learning-enabled framework for the prediction of mechanical properties in new high entropy alloys82Machine Learning–Assisted Design of Material Properties80Multiobjective Machine Learning-Assisted Discovery of a Novel Cyan–Green Garnet: Ce Phosphors with Excellent Thermal Stability73Recent progress in the machine learning-assisted rational design of alloys69Laser-aided additive manufacturing of high entropy alloys: Processes, properties, and emerging applications134Materials Data toward Machine Learning: Advances and Challenges62High‐Throughput Method–Accelerated Design of Ni‐Based Superalloys61Calibration after bootstrap for accurate uncertainty quantification in regression models92Phase Prediction in High Entropy Alloys by Various Machine Learning Modules Using Thermodynamic and Configurational Parameters71Evolution analysis of γ' precipitate coarsening in Co-based superalloys using kinetic theory and machine learning68On the use of transfer modeling to design new steels with excellent rotating bending fatigue resistance even in the case of very small calibration datasets78Material machine learning for alloys: Applications, challenges and perspectives150Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning91Deep learning-based heterogeneous strategy for customizing responses of lattice structures106Design of super-hard high-entropy ceramics coatings via machine learning62Machine-learning-assisted discovery of highly efficient high-entropy alloy catalysts for the oxygen reduction reaction90Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization86Bio-high entropy alloys: Progress, challenges, and opportunities82Machine learning for high-entropy alloys: Progress, challenges and opportunities397Refractory high-entropy alloys: A focused review of preparation methods and properties429Machine learning accelerates the materials discovery142Alloying effects and effective alloy design of high-Cr CoNi-based superalloys via a high-throughput experiments and machine learning framework83Machine-learning-based intelligent framework for discovering refractory high-entropy alloys with improved high-temperature yield strength109Machine learning-based predictions of fatigue life for multi-principal element alloys67Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys62Accelerating the design of high-entropy alloys with high hardness by machine learning based on particle swarm optimization61Corrosion-Resistant Coating Based on High-Entropy Alloys64Light-weight refractory high-entropy alloys: A comprehensive review231Interpretable hardness prediction of high-entropy alloys through ensemble learning81Improving machine learning based phase and hardness prediction of high-entropy alloys by using Gaussian noise augmented data96Accelerating the design of compositionally complex materials via physics-informed artificial intelligence110A neural network model for high entropy alloy design95A map of single-phase high-entropy alloys173Auto-MatRegressor: liberating machine learning alchemists63A review of the multi-dimensional application of machine learning to improve the integrated intelligence of laser powder bed fusion71Recent applications of machine learning in alloy design: A review158Molecular dynamics simulation and machine learning-based analysis for predicting tensile properties of high-entropy FeNiCrCoCu alloys68Design of refractory multi-principal-element alloys for high-temperature applications77Enhancing corrosion-resistant alloy design through natural language processing and deep learning77Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook78High‐performance ferroelectric based materials via high‐entropy strategy: Design, properties, and mechanism96Prediction and design of high hardness high entropy alloy through machine learning79Scope of machine learning in materials research—A review230Prospects of materials genome engineering frontiers71Alloy design for laser powder bed fusion additive manufacturing: a critical review117Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances78Machine learning assisted design of BCC high entropy alloys for room temperature hydrogen storage87MLMD: a programming-free AI platform to predict and design materials63Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective60High-throughput and data-driven machine learning techniques for discovering high-entropy alloys77Different applications of machine learning approaches in materials science and engineering: Comprehensive review68The mastery of details in the workflow of materials machine learning64Advanced Computational Methods for Modeling, Prediction and Optimization—A Review132Artificial Intelligence in Biomaterials: A Comprehensive Review120A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks66Metallurgical aspects of high entropy alloys96Frontiers in high entropy alloys and high entropy functional materials169RETRACTED: Exploring high entropy alloys: A review on thermodynamic design and computational modeling strategies for advanced materials applications95Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives92Optimizing casting process using a combination of small data machine learning and phase-field simulations70Applications of natural language processing and large language models in materials discovery132Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction79AI‐Driven TENGs for Self‐Powered Smart Sensors and Intelligent Devices80Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials68High-Entropy Materials: A New Paradigm in the Design of Advanced Batteries66

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