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RESEARCH PAPER · Computational Materials Science · 2017

An materials informatics approach to Ni-based single crystal superalloys lattice misfit prediction

Xue Feng Jiang, Haiqing Yin, Cong Zhang, Rui-Jie Zhang, Kai-Qi Zhang, Zhenghua Deng, Guoquan Liu, Xuanhui Qu

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An materials informatics approach to Ni-based single crystal superalloys lattice misfit predictionOriginalA numerical model to predict mechanical properties of Ni-base disk superalloys38Machine leaning aided study of sintered density in Cu-Al alloy30A novel approach to predict green density by high-velocity compaction based on the materials informatics method8Modeling of CCT diagrams for tool steels using different machine learning techniques51Study on time-temperature-transformation diagrams of stainless steel using machine-learning approach43The Element Segregation Between γ/γʹ Phases in a Ni-Based Single Crystal Superalloy Studied by 3D-APT and Its Potential Impact on Local Interfacial Misfit Strain26Selecting Doping Elements by Data Mining for Advanced Magnets21A progressive learning method for predicting the band gap of ABO 3 perovskites using an instrumental variable50Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening310A combined machine learning model for the prediction of time-temperature-transformation diagrams of high-alloy steels55Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties137A coherent set of model equations for various surface and interface energies in systems with liquid and solid metals and alloys93Development of Vickers hardness prediction models via microstructural analysis and machine learning50Lattice Misfit Predictions via the Gaussian Process Regression for Ni-Based Single Crystal Superalloys137Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence33Comparing Transfer Learning to Feature Optimization in Microstructure Classification0Identifying key parameters for predicting materials with low defect generation efficiency by machine learning4The Prediction of the Undercooling Degree of As-Cast Irons and Aluminum Alloys via Machine Learning7Machine learning for alloys698Comparing Transfer Learning to Feature Optimization in Microstructure Classification0Comparing Transfer Learning to Feature Optimization in Microstructure Classification0A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels53Machine learning assisted modelling and design of solid solution hardened high entropy alloys97Computational materials design: Composition optimization to develop novel Ni-based single crystal superalloys26Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning17Comparing transfer learning to feature optimization in microstructure classification7Automated pipeline for superalloy data by text mining83Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine Learning31Design of Ni-based turbine disc superalloys with improved yield strength using machine learning18Research on the Relationship between Surface Defects and Preparation Quality Evaluation of Ni-Based Single Crystal Alloy2Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts39Composition design and microstructure of Ni-based single crystal superalloy with low specific weight—numerical modeling and experimental validation11Machine Learning Captures Synthetic Intuitions for Hollow Nanostructures4Machine learning accelerates the materials discovery142Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength56Small atoms fall into bulk from non-close-packed metal surfaces?1Research on TTT Curve of Alloy structural Steel based on Machine Learning3Design Strategy for Art Copper Alloys’ Colors Through Machine Learning and Oxidation Treatment3Machine learning study on time–temperature–transformation diagram of carbon and low-alloy steel10Prediction of sintered density of binary W(Mo) alloys using machine learning27Case Studies on Superalloy Design0Deep learning accelerates the development of Ni-based single crystal superalloys: A physical-constrained neural network for creep rupture life prediction37Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm24Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles9HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API0Alloys innovation through machine learning: a statistical literature review14Discovery of a Ni-based superalloy with low thermal expansion via machine learning12Machine Learning Based Prediction of Creep Life for Nickel-Based Single Crystal Superalloys1Facilitating Recycling of 6xxx Series Aluminum Alloys by Machine Learning-Based Optimization6Machine-Learning-Assisted Phase Prediction in High-Entropy Alloys Using Two-Step Feature Selection Strategy10A review on microstructural stability regulation in nickel-based superalloys: synergistic effects of alloying elements and phase stability optimization22Designing nickel-based superalloys with high creep life and low thermal expansion coefficient using machine learning assisted by multi-objective optimization6A general framework to govern machine learning oriented materials data quality42Lattice misfit design and characterisation in BCC superalloys20Characterization of Two Novel NiTiHf Shape Memory Alloys Designed by Machine Learning Utilizing Novel Experimental Techniques2Review: machine learning approaches for diverse alloy systems46Lattice strain and composition regulated morphology kinetics with antiphase boundary in Co-based superalloy3Investigation of short-range order and mechanical strength of AlCoCrFeNi high-entropy alloys using atomistic simulations and data analytics5Target-driven design of high strength yet corrosion resistant medium Mn steel via interpretable machine-learning8Interpretable prediction of creep life for nickel-based single crystal superalloys integrating physical metallurgy knowledge with an enhanced graph attention network1Machine learning-accelerated reverse design of a novel fine grain nickel-based superalloy with high γ′ volume fraction0Small features, big impact: Influence of overlooked microstructural features on high-entropy alloy predictions5Data‐Driven Approach for Simultaneous Optimization of Strength and Conductivity in Al Alloys0Deepcluster: An advanced design for the prediction of the bimetallic Ni-Al nanoclusters via deep reinforcement learning1Microstructural specificity of optimal creep performance in Ni-based single crystal superalloys under 980°C-250 MPa and 1100°C-137 MPa conditions0

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  2. Machine leaning aided study of sintered density in Cu-Al alloy

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  3. A novel approach to predict green density by high-velocity compaction based on the materials informatics method

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  6. The Element Segregation Between γ/γʹ Phases in a Ni-Based Single Crystal Superalloy Studied by 3D-APT and Its Potential Impact on Local Interfacial Misfit Strain

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  14. Lattice Misfit Predictions via the Gaussian Process Regression for Ni-Based Single Crystal Superalloys

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  15. Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence

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  16. Comparing Transfer Learning to Feature Optimization in Microstructure Classification

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  17. Identifying key parameters for predicting materials with low defect generation efficiency by machine learning

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  18. The Prediction of the Undercooling Degree of As-Cast Irons and Aluminum Alloys via Machine Learning

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  19. Machine learning for alloys

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  20. Comparing Transfer Learning to Feature Optimization in Microstructure Classification

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  42. Deep learning accelerates the development of Ni-based single crystal superalloys: A physical-constrained neural network for creep rupture life prediction

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