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RESEARCH PAPER · International Journal of Minerals Metallurgy and Materials · 2023

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

Guangfei Pan, Feiyang Wang, Chunlei Shang, Hong‐Hui Wu, Guilin Wu, Junheng Gao, Shuize Wang, Zhijun Gao, Xiaoye Zhou, Xinping Mao

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Advances in machine learning- and artificial intelligence-assisted material design of steelsOriginalIntelligent Prediction and Analysis of Mechanical Properties of Magnesium Alloys Using Interpretable Machine Learning1Improved material descriptors for bulk modulus in intermetallic compounds via machine learning27Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel16Exploring interpretable features of hardness for intermetallic compounds prepared by spark plasma sintering9Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology78Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction207State of the art in applications of machine learning in steelmaking process modeling59Predictive Modeling of Tensile Strength in Aluminum Alloys via Machine Learning28Applying Artificial Intelligence Techniques for Optimizing Big Data Retrieval Performance0Descriptors for phase prediction of high entropy alloys using interpretable machine learning36https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties7What Counts as Knowledge in AI-Driven Materials Science? A Philosophical and Practical Reframing0Convergence of machine learning with microfluidics and metamaterials to build smart materials8A quantitative relation for the ductile-brittle transition temperature in pipeline steel27AI energized hydrogel design, optimization and application in biomedicine194Diagnostic Method for Demagnetization Fault of Elevator Synchronous Traction Machine Based on Informer2Machine learning-based performance predictions for steels considering manufacturing process parameters: a review26Data-Driven Aerospace Equipment Based Materials Research Methods1Application of XGBoost and kernel principal component analysis to forecast oxygen content in ESR6Research on hot deformation behavior of Cu-Ti alloy based on machine learning algorithms and microalloying12Critical precipitation behavior of MnTe inclusions in resulfurized steels during solidification11A brief review of machine learning-assisted Mg alloy design, processing, and property predictions38Hydrogen Embrittlement as a Conspicuous Material Challenge─Comprehensive Review and Future Directions405Process metallurgy and data-driven prediction and feedback of blast furnace heat indicators33Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method23A study of forecasting the Nephila clavipes silk fiber's ultimate tensile strength using machine learning strategies14Deep Learning-Powered Optical Microscopy for Steel Research2Machine Learning for Additive Manufacturing of Functionally Graded Materials55Metallurgical Alchemy: Synthesizing Steel Microstructure Images Using DCGANs3Predicting wear resistance of high-carbon Cr-V alloy steel based on machine learning8The interpretable descriptors for fatigue performance of wrought aluminum alloys15Rapid prediction of flow and concentration fields in solid-liquid suspensions of slurry electrolysis tanks9Accelerated intelligent prediction and analysis of mechanical properties of magnesium alloys based on scaled super learner machine-learning algorithms9Machine-learning synergy in high-entropy alloys: A review43Deep Learning and Machine Learning for Materials Design3Validated kinetic modelling of ladle furnace process for predicting inclusions in API steel grade0Plastic Workability and Rheological Stress Model Based on an Artificial Neural Network of SiCp/Al-7.75Fe-1.04V-1.95Si Composites0Application of Combined Machine Learning Methods in Metallurgy FOR Analysis, Selection of Materials and Prediction of Properties2Porous carbon materials for enhanced carbon dioxide capture toward post-combustion: Innovative application and future prospects21The application of machine learning in 3D/4D printed stimuli-responsive hydrogels30Incorporating edge convolution and correlative self-attention into graph neural network for material properties prediction4Steel design based on a large language model57Review of precipitation strengthening in ultrahigh-strength martensitic steel36Integrated Sharing Platform for Genetic Data of Rare and Precious Metal Materials1Prediction Model for Surface Roughness of Galvannealed Strip Based on Industrial Data0Trends in Sustainable Computing and Machine Intelligence in the Hospitality Industry0A New Approach to Predict the Mechanical Properties of Hot‐Rolled Strip Based on Multi‐Dimensional Feature Weighted Similarity and Integrated Just‐In‐Time Learning3Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures7Artificial Intelligence-Aided Design (AIAD) for Structures and Engineering: A State-of-the-Art Review and Future Perspectives22Crystallization prediction and reverse engineering framework construction for mold flux based on machine learning methods1Machine learning-based prediction of hydrogen-assisted fatigue crack growth rate in Cr–Mo steel17Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility6Designing laser powder bed fusion low-alloyed titanium with superior strength-ductility trade-off via machine learning15Artificial intelligence technology in materials selection, device engineering and parameter optimisation for triboelectric nanogenerator4An Optimal Probiotic Carrier: Multiple Steps Toward Selection and Application in Kombucha11Data–Driven Stress Prediction and Microstructure Characterization During Hot Deformation of Al–Zn–Mg–Cu Alloys3Mapping AI Research in North Africa: A Bibliometric Analysis for Trends and Opportunities3Multiphase field modeling of austenite to pearlite–ferrite transformation in hypoeutectoid steel10Automatic Calibration and Optimization Methods of Instruments based on Artificial Intelligence0Image analysis method combined with machine learning for the prediction of soil and air quality3Iron-Based High-Temperature Alloys: Alloying Strategies and New Opportunities19Prediction and analysis of slab front-end bending in hot rolling based on a data-driven method1Possible consequences caused by reducing the number of samples during acceptance tests of a unit of metal products to assess its quality1Review: machine learning approaches for diverse alloy systems46Statistics in enabling 2D materials: Optimization, predictive modelling, and data-driven discovery9Research on Online Traceability Methods for the Causes of Longitudinal Surface Crack in Continuous Casting Slab1The analysis of interactive furniture design system based on artificial intelligence5Application of deep learning for identification of grain boundaries in atomistic simulation data0Enhanced prediction of occurrence forms of heavy metals in tailings: A systematic comparison of machine learning methods and model integration17Physics-informed neural network for simulation of electromagnetic and temperature fields in electroslag remelting process4Computational modelling and optimization of mechanical properties of red brick dust‐epoxy composites using machine learning approach29Rare Earth Elements in Heat-Resistant Magnesium Alloys: Mechanisms, Performance, and Design Strategies16Multiphase-based predictive framework construction of abrasive wear performance in C–V–Cr–Mo alloys2Investigation and machine learning-based prediction of mechanical properties in hybrid natural fiber composites46A new multi-objective optimization design framework for high-temperature alloy steel via machine learning4Advancements in production planning and scheduling within steel manufacturing: A review and its intelligent development12Unraveling the law of element coupling effects on DRX behavior in low-alloy steels: A PCA-based study with XGBoost prediction1Machine-learning-driven synergistic optimization of multi-objective mechanical properties in steel materials2Prediction Model of Crystallization Temperatures of Molten Fluxes for Continuous Casting and Electroslag Remelting Based on Explainable Machine Learning2Physical information-guided ensemble learning model for surface roughness prediction of galvannealed strip2Efficient design of hydrogen-resistant ultra-high-strength steels via active learning and multiscale characterization10Machine learning-based prediction and optimization of performances of porous ZrO2/Al2O3 ceramics: a few-shot learning approach1Processing map for oxide dispersion strengthening Cu alloys based on experimental results and machine learning modelling2Recent advances and perspectives in interface engineering of high-performance alloys2A Life Cycle AI-Assisted Model for Optimizing Sustainable Material Selection7Reinforcement of the Fe(110)/TiN(100) interface by elemental doping: machine learning accelerated first principles calculations0Machine learning-based estimation and optimization of phoenix Dactylifera Seed Powder reinforced vinyl ester bio-composites7Artificial 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 prospects6Knowledge-Guided Interpretable Machine Learning Framework for Ladle Furnace Desulphurisation Control0Rapid Temperature Field Reconstruction for Electroslag Remelting Using Physics-Informed Neural Networks0An Econometric Model for Gold: Prediction Using a Machine Learning Algorithm0Data-Driven Control of Longitudinal Crack in Medium Carbon Steel Slabs: A Causal Learning Framework Integrating DAG and DML0Physical-informed machine learning of phase transformation and application to cooling path optimization1Experimental and ANN approach to predict the mechanical performance of hybrid natural fiber composites for sustainable lightweight engineering applications0Prediction of Yield Strength in 316 Stainless Steel Using a Physics-Informed Parallel Network0Mechanism and data dual-driven multimodal deep learning approach for constitutive relationship modeling of bearing steels2Machine learning enhanced evaluation of mechanical and wear properties in agro-waste/epoxy composites7Advanced machine learning assisted prediction of multi-fiber hybrid natural fiber/epoxy composites based on jute, coconut coir, banana, and pineapple leaf fibers3Large artificial intelligence models for coal mining: Development, applications and challenges0Interpretable Machine Learning Reveals Ni–Mo–Cu Synergistic Corrosion Behavior in Low‐Alloy Steels Under Simulated Marine Atmosphere0Atomic-scale mechanisms of hydrogen trapping in high-strength steel via deep potential molecular dynamics1Utilizing hybrid BIGRU-LIGHTGBM model for mechanical characteristics prediction in aluminium alloys0Mechanical property prediction of medium-heavy plate rolling process based on RF-BO-XGBoost0Heat Treatment Optimization of SS304–Al6061 Composites Using Machine Learning0Multivariable prediction in the hot rolling process using machine learning0AI-assisted quantification of osteogenesis in X-ray microscopy images of biodegradable Mg implants: methodology, benchmarking, and open dataset0Target-Specific Prediction of Critical Variables in Hot Rolling Using Machine-Learning Models0Machine learning for high-performance alloy design: A review0

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