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RESEARCH PAPER · Acta Materialia · 2020

Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening

Hongtao Zhang, Huadong Fu, Xingqun He, Changsheng Wang, Lei Jiang, Long‐Qing Chen, Jianxin Xie

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Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning ScreeningOriginalIntegrating data mining and machine learning to discover high-strength ductile titanium alloys190Effect of in on Fe Phase Morphology and Properties of Cu-Fe Alloy0Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys259Physical Mechanism Interpretation of Polycrystalline Metals’ Yield Strength via a Data-Driven Method: A Novel Hall-Petch Relationship144Quantitative mechanisms behind the synchronous increase of strength and electrical conductivity of cold-drawing oxygen-free Cu wires59A titanium alloys design method based on high-throughput experiments and machine learning65A machine learning based optimization method towards removing undesired deformation of energy-absorbing structures55The Prediction of the Undercooling Degree of As-Cast Irons and Aluminum Alloys via Machine Learning7Study on strengthening effects of Zr-Ti-Nb-O alloys via high throughput powder metallurgy and data-driven machine learning29Modeling solid solution strengthening in high entropy alloys using machine learning254A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys47Multi-objective optimization for materials design with improved NSGA-II95A Property‐Driven Stepwise Design Strategy for Multiple Low‐Melting Alloys via Machine Learning26Machine Learning Accelerated, High Throughput, Multi‐Objective Optimization of Multiprincipal Element Alloys37Age-hardening behavior guided by the multi-objective evolutionary algorithm and machine learning22A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness335Computational materials design: Composition optimization to develop novel Ni-based single crystal superalloys26Prediction of Ti-Zr-Nb-Ta high-entropy alloys with desirable hardness by combining machine learning and experimental data48A Machine Learning Enabled Ultra-Fine Grain Design Strategy of Mg-Mn-Based Alloys23High-cycle fatigue S-N curve prediction of steels based on a transfer learning-guided convolutional neural network9Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algorithm9Pattern Recognition for the Electronic Phase of Bismuth Antimony Thin Films2Composition 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 learning13Improving prediction accuracy of high-performance materials via modified machine learning strategy34Predicting the property contour-map and optimum composition of Cu-Co-Si alloys via machine learning20Mechanical performances and processing-property modeling for Al0.3CoCrFeNiMn high-entropy alloy17Hardness prediction of high entropy alloys with machine learning and material descriptors selection by improved genetic algorithm97Automated pipeline for superalloy data by text mining83Improved strength and conductivity of metallic-glass-reinforced nanocrystalline CuCrZr alloy30Improvement for Ti 3 SiC 2 /Cu joint brazed using composite fillers with abnormal expansion ceramic particulates18Predicting the corrosion properties of cast and hot isostatic pressed CoCrMo/W alloys in seawater by machine learning4Ab initio Cu alloy design for high-gradient accelerating structures5Recent progress in the machine learning-assisted rational design of alloys69Preparation and property optimization of FeCrAl-based ODS alloy by machine learning combined with wedge-shaped hot-rolling31Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction115Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning44High cycle fatigue S-N curve prediction of steels based on transfer learning guided long short term memory network60Creep rupture life prediction of nickel-based superalloys based on data fusion38Machine-learning-assisted discovery of empirical rule for inherent brittleness of full Heusler alloys15Evolution 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 perspectives150Application of explainable artificial intelligence for prediction and feature analysis of carbon diffusivity in austenite8Design of super-hard high-entropy ceramics coatings via machine learning62Establishing reduced-order process-structure linkages from phase field simulations of dendritic grain growth during solidification6Machine learning of the mechanical properties and data-driven design of lead-free solder alloys1High-throughput data mined prediction of design and preparation of flexible carbon-based conductive materials in energy storage4Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy7Extreme Ion‐Transport Inorganic 2D Membranes for Nanofluidic Applications60Towards high entropy alloy with enhanced strength and ductility using domain knowledge constrained active learning47Explainable machine learning in materials science501Hot-deformation kinetics analysis and extrusion parameter optimization of a dilute rare-earth free magnesium alloy29Machine learning assisted design of high-strength Sn-3.8Ag-0.7Cu alloys with the co-additions of Bi and In16Machine learning accelerates the materials discovery142Accelerating phase prediction of refractory high entropy alloys via machine learning11Fatigue life evaluation model for various austenitic stainless steels at elevated temperatures via alloy features‐based machine learning approach18Prediction and optimization of CuCrZr microhardness based on PSO-ensemble learning model0Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys62A Hierarchical Multiscale Crystal Plasticity Model for Strengthening Mechanism and Deformation Behavior of Refractory Multi-Principal Element Alloys0Accelerated discovery of cost-effective Nd–Fe–B magnets through adaptive learning7Accelerated Prediction of Stacking Fault Energy in Fcc Medium Entropy Alloys Using Artificial Neural Networks: Correlation and Feature Analysis1Development of Key Technologies for Intelligent Research and Development of New Materials6Discovery of thermosetting polymers with low hygroscopicity, low thermal expansivity, and high modulus by machine learning35Exploration without Explanation: A Conceptual Critique of Black-Box Materials Discovery0Machine Learning Design of Aluminum-Lithium Alloys with High Strength5Multimodal Data-Driven Design of Low Pressure Die Casting Gating System for Aluminum Alloy Cabin2Attaining exceptional electrical conductivity in Cu-Fe composite by powder rolling strategy38Predicting the hardness of high-entropy alloys based on compositions54Impact Performance Prediction and Optimization of a Circumferentially Corrugated Tube with Variable Wall Thickness Using Support Vector Machine14A Critical Review of Machine Learning Techniques on Thermoelectric Materials66A rapid and effective method for alloy materials design via sample data transfer machine learning101Exploring the relationship between lattice distortion and phase stability in a multi-principal element alloy system based on machine learning method34Cyclic softening in nonlocal shells—A data-driven graph-gradient plasticity approach10Knowledge-aware design of high-strength aviation aluminum alloys via machine learning52Data-driven design of Ni-based turbine disc superalloys to improve yield strength56Prediction Model of Yield Strength of V–N Steel Hot-rolled Plate Based on Machine Learning Algorithm19Data-driven optimization of hardness and toughness of high-entropy nitride coatings23Insight into the Influence of Alloying Elements on the Elastic Properties and Strengthening of Copper: A High-Throughput First-Principles Calculations12Searching for medium entropy alloys with desired mechanical property by adaptive design combined with experiments4Simultaneous enhancement in mechanical and corrosion properties of Al-Mg-Si alloys using machine learning100CALPHAD-assisted composition and processing design of high-strength and high-conductivity copper alloy14Core Electron Count as a Versatile and Accurate New Descriptor for Sorting Mechanical Properties of Diverse Transition Metal Compounds18Interpretable Machine Learning Workflow for Evaluating and Analyzing the Performance of High-Entropy GeTe-Based Thermoelectric Materials25Machine learning assisted phase and size-controlled synthesis of iron oxide particles36Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm24Effect of in on Fe phase morphology and properties of Cu-Fe alloy2Tensile Strength Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning4Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32Comprehensive elemental screening of solid-solution copper alloys1Machine Learning Aided Prediction and Design for the Mechanical Properties of Magnesium Alloys60An Effective Framework for Predicting Performance of Solid-Solution Copper Alloys Using a Feature Engineering Technique in Machine Learning3A multimodal data-driven design of low pressure die casting gating system for aluminum alloy cabin22Divide and conquer: Machine learning accelerated design of lead-free solder alloys with high strength and high ductility37Machine Learning-Based Hardness Prediction of High-Entropy Alloys for Laser Additive Manufacturing42Unveiling glass forming ability patterns in bulk metallic glasses via advanced machine learning approaches10Integrating machine learning and CALPHAD method for exploring low‐modulus near‐β‐Ti alloys47Microstructures, electrical and mechanical properties of an ODS-Cu alloy containing multi-scale complex oxides19Heterogeneous metallic glass composites with a unique combination of strength, plasticity and conductivity17Prediction of the Fatigue Strength of Steel Based on Interpretable Machine Learning25Research on composition-process-property prediction of die casting Al alloys via combining feature creation and attention mechanisms21Prospects of materials genome engineering frontiers71Accelerated design of low-activation high entropy alloys with desired phase and property by machine learning11Alloy design for laser powder bed fusion additive manufacturing: a critical review117Discovery Precision: An effective metric for evaluating performance of machine learning model for explorative materials discovery21Machine learning informed tetragonal ratio c/a of martensite4Machine learning – informed development of high entropy alloys with enhanced corrosion resistance35A Conceptual Blueprint for “Digital Materials Twins” without Simulation: Definitions, Boundaries, and Use-Cases0From Graph Machine Learning to Furnaces: Permittivity Exploring of Dielectric Ceramics0Intelligent processing and development of high-performance automotive aluminum alloys: Application of physics-based and data-driven modeling1Latent Anisotropy: Directional Bias in Materials Embedding Spaces0Machine-learning guided prediction of thermoelectric properties of topological insulator Bi 2 Te 3− x Se x26Data-Driven Study on Thermal Shock Resistance Prediction of Copper Alloys0Accelerated prediction of stacking fault energy in FCC medium entropy alloys using multilayer perceptron neural networks: correlation and feature analysis10Preparation and mechanisms of Cu–Ag alloy fibers with high strength and high conductivity33Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning34Effect of cryorolling on microstructure evolution and mechanical properties of spray deposited Cu-Fe alloy21A hierarchical multiscale crystal plasticity model for refractory multi-principal element alloys28Mitigating the efficiency-voltage trade-off in magnesium air battery via a novel active learning framework10Accelerated design of Al−Zn−Mg−Cu alloys via machine learning34Iterative multi-objective design of hydrogen embrittlement resistant high-strength steels using Bayesian optimization14Predicting and understanding the ductility of BCC high entropy alloys via knowledge-integrated machine learning52Machine Learning-Assisted Prediction of Corrosion Behavior of 7XXX Aluminum Alloys24Prediction of digital transformation of manufacturing industry based on interpretable machine learning12Synchronously enhancing the strength, toughness, and stress corrosion resistance of high-end aluminum alloys via interpretable machine learning156Toward ultra-high strength high entropy alloys via feature engineering42Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature16Estimating the lattice thermal conductivity of AlCoCrNiFe high-entropy alloy using machine learning16Design of high performance Cu-Ni-Si alloys via a multiobjective strategy based on machine learning12A review on copper alloys with high strength and high electrical conductivity230Data-Driven Design of Nickel-Free Superelastic Titanium Alloys4Characterization of CuAg Alloys with Low Ag Concentrations4A brief review of machine learning-assisted Mg alloy design, processing, and property predictions38An ensemble learning strategy for multi‐source hydrogen embrittlement data by introducing missing information5Design of low-alloying and high-performance solid solution-strengthened copper alloys with element substitution for sustainable development8Design strategy to simultaneously enhance electrical conductivity and strength: Cold-drawn copper-based composite wire with in-situ graphene8High-strength and high-conductivity additively manufactured Cu-O alloy enabled by cellular microstructure22Chemical short-range-order induced multiscale strengthening in refractory medium entropy alloys10Heat-resistant aluminum alloy design using explainable machine learning42Data mining accelerated the design strategy of high‐entropy alloys with the largest hardness based on genetic algorithm optimization22Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning14A strategy for high-entropy copper alloys composition design assisted by deep learning based on data reconstruction and network structure optimization17Multi‐objective optimization of three mechanical properties of Mg alloys through machine learning33Understanding thermal transport in magnesium solid solutions through first-principles approaches and machine learning feature screening17Prediction of High-Entropy Alloy Phases Using Soft Computing Techniques2Manipulating TWIP/TRIP via oxygen-doping to synergistically enhance strength and ductility of metastable beta titanium alloys48Quantitative prediction of Mg-RE-Ni alloy corrosion behavior by machine learning33Recent progress on machine learning with limited materials data: Using tools from data science and domain knowledge31Accelerated discovery of lead-free solder alloys with enhanced creep resistance via complementary machine learning strategy8Microstructure and properties of Cu–Ni–Si–Cr alloy fabricated by vacuum-assisted die casting via direct aging11Synergistic-strengthening strategy induce excellent electrical and mechanical properties in Cu/AlCrCuFeNi2.5 composites13

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  50. Towards high entropy alloy with enhanced strength and ductility using domain knowledge constrained active learning

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