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RESEARCH PAPER · npj Computational Materials · 2020

Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses

Zhichao Lu, Zhichao Lu, Xin Chen, De-Ye Lin, Deye Lin, Yuan Wu, Yibo Zhang, Hui Wang, Suihe Jiang, Hongxiang Li, Xianzhen Wang, Zhaoping Lü, Zhaoping Lü

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Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glassesOriginalModelling of phase stability: integrating computational materials science and materials informatics3Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction3A two-step fused machine learning approach for the prediction of glass-forming ability of metallic glasses34Machine learning assisted screening of non-rare-earth elements for Mg alloys with low stacking fault energy23Combined Micromagnetic Simulation and Machine Learning Approach to Analysis of Polycrystalline Bilayer With Exchange Bias4Artificial Intelligence—Engineering Magnetic Materials: Current Status and a Brief Perspective11Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy71A Property‐Driven Stepwise Design Strategy for Multiple Low‐Melting Alloys via Machine Learning26Machine learning prediction of magnetic properties of Fe-based metallic glasses considering glass forming ability75Correlative study between elastic modulus and glass formation in ZrCuAl(X) amorphous system using a machine learning approach4Explicit expressions of the saturation flux density and thermal stability in Fe-based metallic glasses based on Lasso regression17Lowest-energy structural and electronic properties of Cu Zr13− (n = 3–10) clusters in metallic glasses via CALYPSO search and density functional theory calculations25A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness335Prediction of Ti-Zr-Nb-Ta high-entropy alloys with desirable hardness by combining machine learning and experimental data48Corrosion and irradiation behavior of Fe-based amorphous coating in lead-bismuth eutectic liquids15Accelerated design for magnetic high entropy alloys using data-driven multi-objective optimization34Effects of annealing time on nanoscale structural heterogeneity and magnetic properties of Fe80Si9B10Cu1 amorphous alloy1Machine Learning and Python Assisted Design and Verification of Fe–Based Amorphous/Nanocrystalline Alloy7Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks72Progress and Perspective of Metallic Glasses for Energy Conversion and Storage71Phase classification of multi-principal element alloys via interpretable machine learning79Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory23Recent progress in the machine learning-assisted rational design of alloys69A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys33Explainable machine learning in materials science501Fe-based amorphous coating prepared using high-velocity oxygen fuel and its corrosion behavior in static lead-bismuth eutectic alloy14Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118A Property-Oriented Design Strategy of Multi-Principal Element Alloys Via Interpretable Ensemble Learning and Particle Swarm Optimization0Domain knowledge aided machine learning method for properties prediction of soft magnetic metallic glasses32Exploration without Explanation: A Conceptual Critique of Black-Box Materials Discovery0Why Equivariant Networks Fail for Magnetic Ordering: Broken Symmetries and Hidden Degeneracies0Latest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses14A two-step data augmentation method based on generative adversarial network for hardness prediction of high entropy alloy51Prediction of mechanical properties of high-entropy ceramics by deep learning with compositional descriptors19Convolutional neural network guided prediction of saturation magnetic flux density of Fe-based metallic glasses9Multi-objective optimization for high-performance Fe-based metallic glasses via machine learning approach15Methods, progresses, and opportunities of materials informatics72Accelerated discovery of Fe-based amorphous/nanocrystalline alloy through explicit expression and interpretable information based on machine learning21Materials genome strategy for metallic glasses19Insights into metal glass forming ability based on data-driven analysis15Artificial Neural Network Supported Prediction of Magnetic Properties of Bulk Metallic Glasses0A statistics-based study and machine-learning of stacking fault energies in HEAs13Content Analysis of Data on the Thermal Properties of Fluoride and Modified Fluoride Glasses2Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32Контент-анализ данных о термических свойствах фторидных и модифицированных фторидных стекол0Efficient property-oriented optimization of magnetic high-entropy metallic glasses via a multi-stage design strategy5Oxide dependent textured crystallization on amorphous alloy surface8Failure Modes in Materials AI Explanations: A Conceptual Catalog and Prevention Principles0Ferromagnetic Functional Materials1Optimizing Glass Formation Ability and Magnetic Flux Density of High-Fe-Content Fe-Based Amorphous Alloys Using Interpretable Machine Learning0Interpretable machine learning-assisted design of Fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity39XGBoost algorithm-assisted prediction of amorphous formability of Fe-based nanocrystalline precursor5Deep learning-based prediction of saturation magnetic flux density in Fe-based metallic glasses via transfer learning5Machine learning assisted optimization of soft magnetic properties in ternary Fe–Si–Al alloys16Highly effective design of high GFA alloys with different metal-based and various components by machine learning14Glass forming ability prediction of bulk metallic glasses based on fused strategy5Determinants of saturation magnetic flux density in Fe‐based metallic glasses: insights from machine‐learning models20Feature engineering mediated machine learning prediction of high-frequency property of soft magnetic thin films1Machine learning studies for magnetic compositionally complex alloys: A critical review33Composition design and crystallization behavior of Zr–Cu–Ni–Al bulk metallic glasses3Machine learning assisted study of phase and properties in cobalt-free AlCrxCuFeNi2 high-entropy alloys13Exploring Microstructure and Magnetic Domain Evolution in the As-cast Soft Magnetic Fe74B20Nb2Hf2Si2 Alloy: A Comprehensive Study Using STEM, Lorentz TEM, and LM-STEM DPC Microscopy8A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning18Machine learning assisted design and preparation of Fe85Si2B8.5P3.5C1 amorphous/nanocrystalline alloy with high B and low H12A brief review on Ce and Zr-based phase-separated metallic glasses5Machine Learning Application for Functional Properties Prediction in Magnetic Materials7Predicting miscibility in binary compounds: a machine learning and genetic algorithm study1Generative model-based inverse design of Fe-based metallic glasses with high saturation magnetic flux density8Algorithmic Simplicity as Scientific Virtue: A Conceptual Tension in Materials AI Design0Algorithmic Steering Without Consent: Control Asymmetries in AI-Directed Materials Exploration0Predicting the magnetic properties of Fe-based bulk metallic glasses by ensemble machine learning and interpretable information5Convolutional neural network-based prediction of hardness in bulk metallic glasses with small data3A Review of Artificial Intelligence‐Driven Innovations in Soft Magnetic Materials Optimization: Current Trends and Future Horizons13Extremely wide annealing processing window in Fe-based amorphous/nanocrystalline alloy guided by machine learning6Machine learning approach for predicting the fracture toughness of bulk metallic glasses2Probability stability analysis of active trapdoors beneath spatially random anisotropic clays0Bearing capacity prediction of open caissons in two-layered clays using five tree-based machine learning algorithms11Discovery of high Fe content amorphous alloys with desired soft magnetic properties by incremental machine learning12Metallic glass roadmap26Unraveling the anomalous nano‐crystallization behavior of FeNi‐based amorphous alloy via in situ high‐energy synchrotron X‐ray diffraction7Multi-objective optimization of Fe-based metallic glasses on magnetic property, thermal stability and glass forming ability3Geçmişten Günümüze Kütlesel Metalik Camlar ve Yenilikçi Uygulamaları0Cooperative game of composition, structure, and properties in rare earth bearing melts: Molecular dynamics simulation and interpretable machine learning study3Artificial intelligence can recognize metallic glasses in vast compositional space with sparse data5Investigate the composition effect of Cu-Nb-B-Fe soft magnetic metallic glasses using deep learning method1Soft magnetic amorphous alloys and their derivatives22Graph learning metallic glass discovery from Wikipedia2Data-driven optimization strategy for the mechanical properties of ZM5 alloy0A deep recurrent learning framework for predicting saturation magnetic flux density in Fe-based metallic glasses1Generative AI inversely designs amorphous alloys with customized properties0Human Oversight as System Design: A Conceptual Reframing of Control in Semi-Autonomous Materials AI0Nanoscale Phase Separation and Properties of Zr-Based Metallic Glasses: An Overview0AI ‐Driven Precision Annealing for High Performance Fe‐Based Amorphous Alloys5Prediction of impact damage to Fe-based amorphous coatings via interpretable machine learning0Towards intelligent design of metallic glasses: a data-driven pathway for closing the theory-experiment loop1Design of Nanocrystalline Alloy for Reducing Motor Hysteresis Loss by Machine Learning0Machine learning in magnetism: Progress, challenges, and perspectives2Deciphering the composition–annealing coupling and crystallization kinetics in Fe–Si–B–P–Cu alloys via interpretable symbolic regression0Multi-task deep-learning optimization of trade-off properties for superior-performance Fe-based soft magnetic alloys0Prediction of coercivity and saturation magnetization of strontium hexagonal ferrites using machine learning0Interpretable machine learning for functional properties prediction of Fe–Si–Al alloys via feature engineering0Materials informatics across the length scales0Transformative marketing in the age of smart data: current trends and future research pathways0Local Structural Distortions and Frustrated Magnetism in Slag-Derived Lithium Borate Glasses0Efficient screening of low liquidus Ti-based amorphous brazing filler metals via machine learning with limited experimental iteration1Closed-loop active-inverse learning for constrained discovery of high-resistivity Fe-based amorphous alloys0Tuning structural symmetry and magnetic properties in phosphate glass-ceramics via slag incorporation0Modelling magnetic material properties with uncertainty-aware neural networks0Why machine learning struggles with magnetism? The role of temperature, disorder, and spin physics0Quantitative descriptor-dependent design of high-Bs Fe-based amorphous alloys using an interpretable machine learning framework0

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