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RESEARCH PAPER · Metals · 2024

Machine Learning Design for High-Entropy Alloys: Models and Algorithms

S. Liu, Chao Yang

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Machine Learning Design for High-Entropy Alloys: Models and AlgorithmsOriginalExploring new pathways to enhance high entropy alloys properties0Unsupervised learning and pattern recognition in alloy design15CoCrFeMnNi0.8V/Cr3C2-Ni20Cr High-Entropy Alloy Composite Thermal Spray Coating: Comparison with Monolithic CoCrFeMnNi0.8V and Cr3C2-Ni20Cr Coatings7Predictive analytics of wear performance in high entropy alloy coatings through machine learning31Multicomponent alloys design and mechanical response: From high entropy alloys to complex concentrated alloys71Microstructure and properties of Cu-3Ti-0.3Cr-0.15Mg alloy designed via machine learning35The path towards plasma facing components: A review of state-of-the-art in W-based refractory high-entropy alloys39Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review62Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization41Machine Learning for the Design of High Entropy Alloys: A Review3BaTiO3-based high-entropy ceramics for enhanced capacitive energy storage performance18AI-Driven Inverse Design of Materials: Past, Present, and Future78Inverse design of high-performance piezoelectric semiconductors via advanced crystal representation and large language models1Machine Learning-Based Computational Design Methods for High-Entropy Alloys49Batch active learning for microstructure–property relations in energetic materials4Prediction of mechanical properties of high entropy alloys based on machine learning4High‐throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi‐principal element alloys835Chapter 2 High-entropy alloys: design, properties, manufacturing, and applications1Data‐Driven Design of Spinodal Decomposition in (Ti, Zr, Hf)C Composite Carbides for Optimizing the Hardness‐Toughness Trade‐Off6Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators3AI-driven optimisation of metal alloys for space applications4Data Science in Order and Disorder of High-Entropy Materials4Research Progress on Process Optimization of Metal Materials in Wire Electrical Discharge Machining5Intelligent Design and Simulation of High-Entropy Alloys via Machine Learning and Multiobjective Optimization Algorithms5Review of machine learning-assisted multi-property design of high-entropy alloys: phase structure, mechanical, tribological, corrosion, and hydrogen storage properties18Microstructural characterization and hardness prediction of AlCuCrFeNi high entropy alloys using Transformer-based Physics-Informed Neural Networks (T-PINN)4Ensembles of Graph and Physics-Informed Machine Learning for Scientific Modeling in Materials Science: A Review11Uncertainty-Aware Design of High-Entropy Alloys via Ensemble Thermodynamic Modeling and Search Space Pruning2AI Design for High Entropy Alloys: Progress, Challenges and Future Prospects16Artificial intelligence in materials science and chemistry: Past, present and future trajectories9Supervised machine learning for corrosion assessment of multi-principal element alloys using experimental and generative datasets8High-entropy alloy electrocatalysts for water splitting: A systematic review with perspectives on machine learning and future design strategies9Accelerated design of 5xxx-Series Al-Mg alloys with enhanced mechanical properties via interpretable machine learning and microstructural validation0A Mini Review on Evolution of High‐Entropy Alloy Design: From Experimental Approaches to Machine Learning Integration4Tribological properties of non-equiatomic FeCoCrNiMn high entropy alloys: Molecular dynamics simulations and machine learning predictions38Machine learning-enhanced constitutive modeling of hot deformation in FeCr1.2Ni1.5Al multi-principle element alloys: A denoising neural network approach7Quantum Computers and Hybrid Machine Learning Models for the Discovery of Lightweight Structural Alloys0Predicting vacancy formation energies in refractory non-dilute random alloys using improved graph-based machine learning models trained on density functional theory data1A Physics-Guided Surrogate Framework for Sustainability-Aware Screening of High-Entropy Alloys1A Comprehensive Review of the Computational Approaches for Structure–Property Prediction of High Entropy Materials0Research on the microstructures and mechanical properties intelligent NiTi-based high entropy shape memory thin film on 304 stainless steel fabricated by DC and RF magnetron sputtering0RETRACTED: Deepcluster: A selective and stable strategy to predict the AuPt-based high entropy alloy nanoclusters via deep reinforcement learning0Improving the corrosion resistance of structural materials in liquid lead-bismuth eutectic alloys: Combined machine learning and molecular dynamics1Discovery of single solid solution multi-principal element alloys: synergy of semi-empirical thermodynamics and Ab initio evolutionary algorithms0Machinability of complex concentrated alloys for bio-implants: Roughness analysis after machining0Data-driven and intelligent design of high-entropy alloys: A review of machine learning across composition, microstructure, and properties0Nanostructure strengthening strategies for high-entropy alloys: From microstructure design to outstanding performance3Transforming high entropy alloy designing with predictive ensemble machine learning: A framework for accurate micro-hardness estimation and interpretability0Machine Learning‐Assisted Design of the Al–Co–Cr–Fe–Ni–Ti‐Based High‐Entropy Alloys With Target Room‐Temperature Tensile Elongation0Phase-stability-guided explainable machine learning for mechanical-property prediction and experimentally supported candidate screening of high-entropy alloys0

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  2. Unsupervised learning and pattern recognition in alloy design

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  4. Predictive analytics of wear performance in high entropy alloy coatings through machine learning

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  5. Multicomponent alloys design and mechanical response: From high entropy alloys to complex concentrated alloys

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  7. The path towards plasma facing components: A review of state-of-the-art in W-based refractory high-entropy alloys

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  10. Machine Learning for the Design of High Entropy Alloys: A Review

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  14. Machine Learning-Based Computational Design Methods for High-Entropy Alloys

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  15. Batch active learning for microstructure–property relations in energetic materials

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  16. Prediction of mechanical properties of high entropy alloys based on machine learning

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  17. High‐throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi‐principal element alloys

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  18. 35Chapter 2 High-entropy alloys: design, properties, manufacturing, and applications

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