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RESEARCH PAPER · Acta Materialia · 2019

Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models

Yan Zhang, Cheng Wen, Changxin Wang, Stoichko Antonov, Dezhen Xue, Yang Bai, Yanjing Su

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Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning modelsOriginalMachine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research104Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys213A single-phase ScTiZrHf high-entropy alloy with thermally stable hexagonal close-packed structure53Machine 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 alloys135Machine learning assisted multi-objective optimization for materials processing parameters: A case study in Mg alloy119Theories for predicting simple solid solution high-entropy alloys: Classification, accuracy, and important factors impacting accuracy84Microstructure and mechanical properties of non equiatomic FeCoNiCuMo high entropy systems3Coupling physics in machine learning to predict properties of high-temperatures alloys95Multicomponent high-entropy Cantor alloys877Frontiers in atomistic simulations of high entropy alloys68Machine learning-assisted discovery of strong and conductive Cu alloys: Data mining from discarded experiments and physical features86Designing thermal functional materials by coupling thermal transport calculations and machine learning33Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence33Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach91Yield strength prediction of high-entropy alloys using machine learning130Recent progress of high-entropy materials for energy storage and conversion592Alloy design and properties optimization of multi-component alloy based on solidification characteristics26Navigating the Complex Compositional Landscape of High-Entropy Alloys2Visualizing Temperature-Dependent Phase Stability in High Entropy Alloys3Generalized small set of ordered structures method for the solid-solution phase of high-entropy alloys18High-throughput calculations based on the small set of ordered structures method for non-equimolar high entropy alloys15Machine learning-based prediction of phases in high-entropy alloys149Accelerating materials discovery using machine learning238A Personal Perspective on the Discovery and Significance of Multicomponent High-Entropy Alloys5Harnessing the Complex Compositional Space of High-Entropy Alloys9High-entropy energy materials: challenges and new opportunities960Machine Learning for High-Entropy Alloys10Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation34Mechanical behavior of high-entropy alloys1321Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study133Modeling of Alloying Effect on Isothermal Transformation: A Case Study for Pearlitic Steel18Machine learning prediction of glass-forming ability in bulk metallic glasses58Improving phase prediction accuracy for high entropy alloys with Machine learning109Predictive descriptors in machine learning and data-enabled explorations of high-entropy alloys73Application of deep transfer learning to predicting crystal structures of inorganic substances33High-entropy ceramics: Present status, challenges, and a look forward1109Tungsten-containing high-entropy alloys: a focused review of manufacturing routes, phase selection, mechanical properties, and irradiation resistance properties64Modeling solid solution strengthening in high entropy alloys using machine learning254A focused review on machine learning aided high-throughput methods in high entropy alloy134Structure prediction in high-entropy alloys with machine learning48A tool to predict the evolution of phase and Young’s modulus in high entropy alloys using artificial neural network40Accelerated and conventional development of magnetic high entropy alloys207Learning phase selection and assemblages in High-Entropy Alloys through a stochastic ensemble-averaging model33Molecular structure prediction based on graph convolutional networks.0Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications54A Review for Machine Learning Applications in Characterizing Biomaterials and Biological Materials Properties3Machine Learning to Predict Quasicrystals from Chemical Compositions62Machine learning for alloys698Microstructure stability during high temperature deformation of CoCrFeNiTa eutectic high entropy alloy through nano-scale precipitation54Developing a multilateral-based neural network model for engineering of high entropy amorphous alloys8Rapid screening of high-entropy alloys using neural networks and constituent elements34Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys11Using multiple regression analysis to predict directionally solidified TiAl mechanical property24Machine learning-based prediction of phases in high-entropy alloys: A data article57Differentiable thermodynamic modeling17Recent machine learning guided material research - A review12Visualizing temperature-dependent phase stability in high entropy alloys46High-Entropy Alloys Properties Prediction Model by Using Artificial Neural Network Algorithm5Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges67Design Strength-Ductility Synergy of Metastable High-Entropy Alloys by Tailoring Unstable Fault Energies0A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels53A strategy assisted machine learning to process multi-objective optimization for improving mechanical properties of carbon steels95Machine learning assisted modelling and design of solid solution hardened high entropy alloys97Machine learning prediction of thermodynamic and mechanical properties of multicomponent Fe-Cr-based alloys24High‐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 hardness335Oxidation behavior of Al0.2CoCrFeNi high‐entropy alloy film in supercritical water environment21On the phase constituents of three CoCrFeNiX (X = Cr, Mo, W) high-entropy alloys after prolonged annealing16Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System42On the phase constituents of three CoCrFeNiX (X = B, Al, Ga) high-entropy alloys after prolonged annealing18A mini review of machine learning in inorganic phosphors10A thermodynamic database of the Ni-Mo-Re system5Accelerated design for magnetic high entropy alloys using data-driven multi-objective optimization34Data-Driven Phase Selection, Property Prediction and Force-Field Development in Multi-Principal Element Alloys9Estimating the performance of a material in its service space via Bayesian active learning: a case study of the damping capacity of Mg alloys14Machine-learning and high-throughput studies for high-entropy materials165Machine-Learning Prediction of Vegard's Law Factor and Volume Size Factor for Binary Substitutional Metallic Solid Solutions31New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems24On the Conditions under Which Sequential Learning Fails: Catastrophic Forgetting in Compositionally Complex Alloys0Rapid Discovery of High Hardness Multi Principal Element Alloys Using a Generative Adversarial Network Model48SISSO-assisted prediction and design of mechanical properties of porous graphene with a uniform nanopore array25Composition design of high yield strength points in single-phase Co–Cr–Fe–Ni–Mo multi-principal element alloys system based on electronegativity, thermodynamic calculations, and machine learning13Predicting the property contour-map and optimum composition of Cu-Co-Si alloys via machine learning20Vacancy dependent mechanical behaviors of high-entropy alloy48Design high-entropy carbide ceramics from machine learning149Hardness prediction of high entropy alloys with machine learning and material descriptors selection by improved genetic algorithm97Automated pipeline for superalloy data by text mining83Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys104Phase classification of multi-principal element alloys via interpretable machine learning79A multiple loops machine learning framework to predict the properties of WC–Co based cemented carbides17Molecular distance matrix prediction based on graph convolutional networks4Modeling environment-dependent atomic-level properties in complex-concentrated alloys9Development of machine learning based models for design of high entropy alloys13Predicting the corrosion properties of cast and hot isostatic pressed CoCrMo/W alloys in seawater by machine learning4Symbolic regression in materials science via dimension-synchronous-computation41Machine 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 alloys69Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network15Active learning and molecular dynamics simulations to find high melting temperature alloys40Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine Learning31Integrating atomistic simulations and machine learning to design multi-principal element alloys with superior elastic modulus20Insights on phase formation from thermodynamic calculations and machine learning of 2436 experimentally measured high entropy alloys41Materials Data toward Machine Learning: Advances and Challenges62HEAPS: A user-friendly tool for the design and exploration of high-entropy alloys based on semi-empirical parameters93Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys21Phase Prediction of High-Entropy Alloys by Integrating Criterion and Machine Learning Recommendation Method18Predicting the formation and stability of oxide perovskites by extracting underlying mechanisms using machine learning24Phase Prediction Study of High-Entropy Energy Alloy Generation Based on Machine Learning5Phase 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 learning68Material machine learning for alloys: Applications, challenges and perspectives150High Entropy Materials - Microstructures and Properties16Phase prediction and effect of intrinsic residual strain on phase stability in high-entropy alloys with machine learning56Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning91A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys33A first-principles-based high fidelity, high throughput approach for the design of high entropy alloys31Distilling physical origins of hardness in multi-principal element alloys directly from ensemble neural network models33Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach24Ferroelasticity in Two-Dimensional Tetragonal Materials46The intrinsic strength prediction by machine learning for refractory high entropy alloys25Interval prediction machine learning models for predicting experimental thermal conductivity of high entropy alloys16Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts39Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization86Design metastability in high-entropy alloys by tailoring unstable fault energies64Simulation and Calculation for Predicting Structures and Properties of High-Entropy Alloys12Data-driven based phase constitution prediction in high entropy alloys26Machine learning for high-entropy alloys: Progress, challenges and opportunities397Towards high entropy alloy with enhanced strength and ductility using domain knowledge constrained active learning47Overview: recent studies of machine learning in phase prediction of high entropy alloys56Phase prediction in high-entropy alloys with multi-label artificial neural network17Complex Concentrated Alloys: A Cornucopia of Possible Structural and Functional Applications9Machine learning accelerated design of non-equiatomic refractory high entropy alloys based on first principles calculation42Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning4Machine Learning Captures Synthetic Intuitions for Hollow Nanostructures4Hierarchical machine learning based structure–property correlations for as–cast complex concentrated alloys16Refractory high-entropy alloys: A focused review of preparation methods and properties429Rational Design for Efficient Bifunctional Oxygen Electrocatalysts by Artificial Intelligence7Phase Stability Through Machine Learning23Machine learning accelerates the materials discovery142Rapid Discovery of Efficient Long-Wavelength Emission Garnet:Cr NIR Phosphors via Multi-Objective Optimization56A User’s Guide to Machine Learning for Polymeric Biomaterials99Machine learning prediction of the mechanical properties of refractory multicomponent alloys based on a dataset of phase and first principles simulation4Design of Light-Metal Alloys Using Machine Learning Techniques0FCC vs. BCC phase selection in high-entropy alloys via simplified and interpretable reduction of machine learning models26Prediction of High-entropy Alloy Hardness Based on Ensemble Learning Combination Algorithm3An integrated machine learning model for accurate and robust prediction of superconducting critical temperature28Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys62

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