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RESEARCH PAPER · International Journal of Minerals Metallurgy and Materials · 2022

Recent progress in the machine learning-assisted rational design of alloys

Huadong Fu, Hong‐Tao Zhang, Changsheng Wang, Wei Yong, Jianxin Xie

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Recent progress in the machine learning-assisted rational design of alloysOriginalCreep rupture life prediction of nickel-based superalloys based on data fusion38Fatigue life evaluation model for various austenitic stainless steels at elevated temperatures via alloy features‐based machine learning approach18Intelligent Prediction and Analysis of Mechanical Properties of Magnesium Alloys Using Interpretable Machine Learning1Physics-Constrained Neural Networks for Predicting Phase Transformation Pathways in Multi-Component Alloys0Progress on improving strength-toughness of ultra-high strength martensitic steels for aerospace applications: a review240Rational design of high-entropy ceramics based on machine learning – A critical review52Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations40Advances in machine learning- and artificial intelligence-assisted material design of steels109Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems33Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel16Alloying element distributions of precipitates in Cu–Cr alloys aided by machine learning10Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology78Multi‐objective optimization and its application in materials science46Development of High-Strength Mg–Gd–Y Alloy Based on Machine Learning Method2Intelligent processing and development of high-performance automotive aluminum alloys: Application of physics-based and data-driven modeling1Unsupervised learning and pattern recognition in alloy design15Advancements in machine learning for material design and process optimization in the field of additive manufacturing48Prediction of creep rupture life of ODS steels based on machine learning13Data-Driven Aerospace Equipment Based Materials Research Methods1Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature16Optimized design of composition and brazing process for Cu-Ag-Zn-Mn-Ni-Si-B-P alloy brazing material based on machine learning strategy to improve brazing properties9Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method23Stabilizing dual-cation liquid metal battery for large-scale energy storage: A comprehensive hybrid design approach15Unraveling Magnesium Alloy Corrosion Patterns Through Unsupervised Machine Learning: Exploring Clustering Techniques for Enhanced Insight16Multi-objective optimization of multi-principal element alloys via high-throughput simulation and active learning10The mastery of details in the workflow of materials machine learning64Recent progress on machine learning with limited materials data: Using tools from data science and domain knowledge31A review on AI-driven environmental-assisted stress corrosion cracking properties of conventional and advanced manufactured alloys8A machine learning method based on TPE-XGBoost model for TRIP/TWIP near-β titanium alloy design11Accelerated intelligent prediction and analysis of mechanical properties of magnesium alloys based on scaled super learner machine-learning algorithms9Empowering the Sustainable Development of High‐End Alloys via Interpretive Machine Learning52Accelerating spin Hall conductivity predictions via machine learning6Machine learning design of 400 MPa grade biodegradable Zn-Mn based alloys with appropriate corrosion rates16Development of a universal multimodal prediction method to optimise process parameters for improving densification during laser powder bed fusion14Multi-model integration accelerates Al-Zn-Mg-Cu alloy screening9Inverse prediction of Al alloy post-processing conditions using classification with guided oversampling3Machine Learning-Based Methods for Materials Inverse Design: A Review34Designing the counter pressure casting gating system for a large thin-walled cabin by machine learning1Fitting-free mechanical response prediction in dual-phase steels by crystal plasticity theory guided deep learning16Potential and progress of two-dimensional nanomaterials in oil-based lubrication10Leveraging molecular dynamics and machine learning to predict impact performance in polycrystalline magnesium alloys6Inverse Engineering of Mg Alloys Using Guided Oversampling and Semi‐Supervised Learning1A review on microstructural stability regulation in nickel-based superalloys: synergistic effects of alloying elements and phase stability optimization22Effects of composition and natural ageing on microstructure and mechanical properties of Al-Mg-Si heat treatment-free die-cast alloys assisted with machine learning5Review: strengthening and toughening mechanisms of high-strength steels for deep-sea pressure vessel hulls14Artificial intelligence for post-moore field-effect transistors: a review and perspective1Directly Predicting Hot Ductility of Steels Using Machine Learning Approaches1Data-driven multi-process modeling and integrated design strategy for complex high-performance alloys12Surface Modification of Porous Orthopedic Medical Implants Produced by Additive Manufacturing0Machine Learning Approach for Discovering Titanium Alloys with a Low Young’s Modulus0Machine-learning-driven synergistic optimization of multi-objective mechanical properties in steel materials2Artificial intelligence in the development of structural and functional materials: A transformative frontier and complementary enabler of the rational alloy design framework10Machine learning assisted optimization design and microstructure-property evolution mechanisms of 600 °C high temperature burn resistant titanium alloys7Machine learning-based prediction of pore characteristics in laser powder bed fusion-fabricated 316L stainless steel0Advances in graph neural networks for alloy design and properties predictions: a review8Compositional design of Ni-based superalloys for additive manufacturing: Progress and perspectives9Predicting grain growth kinetic in steels using machine learning and XAI for mechanical properties2Artificial intelligence-assisted non-metallic inclusion particle analysis in advanced steels using machine learning: A review6Transfer learning-enabled performance prediction of metallic materials: Methods, applications and prospects6Machine Learning for Alloy Design: A Property-Oriented Review3Machine learning applications in metallic materials: Recent advances and future perspectives8Laser powder bed fusion of zinc-based biomaterials: Process-microstructure-property relationships for biomedical applications0Machine-learning-driven optimization of CMT welding for joint–base-metal property equivalence in thin 5052 aluminum alloy sheets1Machine Learning–Based Prediction of the Constitutive Model of Austenitic Stainless Steels at Room and Elevated Temperatures0Recent Advancements in Aluminum Alloy Research: Integrating Traditional Metallurgy with Machine Learning and Data-Driven Approaches3Phase equilibria and wear mechanism of Cu-Cr-Zr-Si alloy: Insights from CALPHAD method and experimental investigations1Interpretable multimodal modeling of local microstructure–property relationships in ultra-high-strength steels0

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  1. Creep rupture life prediction of nickel-based superalloys based on data fusion

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  2. Fatigue life evaluation model for various austenitic stainless steels at elevated temperatures via alloy features‐based machine learning approach

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  3. Intelligent Prediction and Analysis of Mechanical Properties of Magnesium Alloys Using Interpretable Machine Learning

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  4. Physics-Constrained Neural Networks for Predicting Phase Transformation Pathways in Multi-Component Alloys

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  5. Progress on improving strength-toughness of ultra-high strength martensitic steels for aerospace applications: a review

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  6. Rational design of high-entropy ceramics based on machine learning – A critical review

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  7. Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

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  8. Advances in machine learning- and artificial intelligence-assisted material design of steels

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  9. Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems

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  10. Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel

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  11. Alloying element distributions of precipitates in Cu–Cr alloys aided by machine learning

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  12. Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations

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  14. Multi‐objective optimization and its application in materials science

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  15. Development of High-Strength Mg–Gd–Y Alloy Based on Machine Learning Method

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  16. Intelligent processing and development of high-performance automotive aluminum alloys: Application of physics-based and data-driven modeling

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  17. Unsupervised learning and pattern recognition in alloy design

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  18. Advancements in machine learning for material design and process optimization in the field of additive manufacturing

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  19. Prediction of creep rupture life of ODS steels based on machine learning

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  20. Data-Driven Aerospace Equipment Based Materials Research Methods

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  22. Optimized design of composition and brazing process for Cu-Ag-Zn-Mn-Ni-Si-B-P alloy brazing material based on machine learning strategy to improve brazing properties

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  23. Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

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  25. Unraveling Magnesium Alloy Corrosion Patterns Through Unsupervised Machine Learning: Exploring Clustering Techniques for Enhanced Insight

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  37. Inverse prediction of Al alloy post-processing conditions using classification with guided oversampling

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  38. Machine Learning-Based Methods for Materials Inverse Design: A Review

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