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RESEARCH PAPER · Computational Materials Science · 2018

Two-way design of alloys for advanced ultra supercritical plants based on machine learning

Xiaobing Hu, Jincheng Wang, Yanyan Wang, Junjie Li, Zhijun Wang, Yingying Dang, Yuefeng Gu

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42 direct citing works

Two-way design of alloys for advanced ultra supercritical plants based on machine learningOriginalUltra-high temperature ceramics melting temperature prediction via machine learning79A property-oriented design strategy for high performance copper alloys via machine learning226Uncovering the eutectics design by machine learning in the Al–Co–Cr–Fe–Ni high entropy system275Metaheuristic-based inverse design of materials – A survey46Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging89A screening strategy for hot forging combining high-throughput forging experiment and machine learning3Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach91Dirty engineering data-driven inverse prediction machine learning model24Machine learning assisted discovery of new thermoset shape memory polymers based on a small training dataset81Discovery of Lead-Free Hybrid Organic/Inorganic Perovskites Using Metaheuristic-Driven DFT Calculations30Strategies Regarding High-Temperature Applications w.r.t Strength, Toughness, and Fatigue Life for SA508 Alloy4A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys47Machine learning for alloys698Continually Reactivating Iterative-Projection Process Method for Instantiating Microstructure from Two-Point Statistics0Machine Learning-Based Prediction for Time Series Damage Evolution of Ni-Based Superalloy Microstructures0Machine Learning-Based Prediction for Time Series Damage Evolution of Ni-Based Superalloy Microstructures0Structure-property modeling scheme based on optimized microstructural information by two-point statistics and principal component analysis21Hardness prediction of high entropy alloys with machine learning and material descriptors selection by improved genetic algorithm97Global‐Oriented Strategy for Searching Ultrastrength Martensitic Stainless Steels11Design of a NiTiHf shape memory alloy with an austenite finish temperature beyond 400 °C utilizing artificial intelligence35Shape memory elastomers: A review of synthesis, design, advanced manufacturing, and emerging applications41Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments36Continually reactivating iterative-projection method for instantiating microstructure from two-point statistics7Machine learning-based prediction for time series damage evolution of Ni-based superalloy microstructures4Machine learning-based inverse design for single-phase high entropy alloys28Prediction of mechanical properties of Mg-rare earth alloys by machine learning16An interface-oriented data-driven scheme applying into eutectic patterns evolution4Directed energy deposition combining high-throughput technology and machine learning to investigate the composition-microstructure-mechanical property relationships in titanium alloys46Machine learning accelerates the materials discovery142Three-step learning strategy for designing 15Cr ferritic steels with enhanced strength and plasticity at elevated temperature6A Genetic Algorithm-Optimized Neural Network for Chlorophyll a Estimation Using MODIS Satellite Data in Coastal Water: Application to the Sinpho Bay of DPR Korea5Case Studies on Superalloy Design0Artificial intelligence inspired design of non-isothermal aging for γ–γ′ two-phase, Ni–Al alloys8Machine learning assisted design of novel refractory high entropy alloys with enhanced mechanical properties48Evaluating creep rupture life in austenitic and martensitic steels with soft-constrained machine learning9Alloys innovation through machine learning: a statistical literature review14Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation11Design of high performance Cu-Ni-Si alloys via a multiobjective strategy based on machine learning12Combined approach to capture the evolution of oxidation of Nickel based superalloys using data driven approaches7Data mining accelerated the design strategy of high‐entropy alloys with the largest hardness based on genetic algorithm optimization22A machine learning framework for the prediction of grain boundary segregation in chemically complex environments12Creep Lifetime Prediction of Alloy 617 Using Black Box Machine Learning Approach3Physics-coupled data-driven design of high-temperature alloys12Discovering chemistry to creep rupture equations in Alloy 617 with machine learning3A phase prediction strategy of high entropy alloys manufactured by selective laser melting based on feature selection and machine learning12A review on thermal expansion control in Fe–Ni–Cr austenitic alloys: From Invar effect to advanced power systems1Machine learning-driven new paradigm for Co-based superalloys3Genetic algorithm coupled with thermodynamic calculations for high-strength nickel-based corrosion-resistant alloys design0Deepcluster: An advanced design for the prediction of the bimetallic Ni-Al nanoclusters via deep reinforcement learning1A Precipitation Strengthening Parameter Driven Design Strategy for Aging Hardened Nickel-Based Corrosion-Resistant Alloys: Thermodynamics-Genetic Algorithm Integration0

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  1. Ultra-high temperature ceramics melting temperature prediction via machine learning

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  2. A property-oriented design strategy for high performance copper alloys via machine learning

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  3. Uncovering the eutectics design by machine learning in the Al–Co–Cr–Fe–Ni high entropy system

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  4. Metaheuristic-based inverse design of materials – A survey

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  5. Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging

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  6. A screening strategy for hot forging combining high-throughput forging experiment and machine learning

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  7. Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach

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  8. Dirty engineering data-driven inverse prediction machine learning model

    Scientific Reports · 2020-11-24

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  9. Machine learning assisted discovery of new thermoset shape memory polymers based on a small training dataset

    Polymer · 2020-12-30

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  10. Discovery of Lead-Free Hybrid Organic/Inorganic Perovskites Using Metaheuristic-Driven DFT Calculations

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  11. Strategies Regarding High-Temperature Applications w.r.t Strength, Toughness, and Fatigue Life for SA508 Alloy

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  12. A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys

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  13. Machine learning for alloys

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  14. Continually Reactivating Iterative-Projection Process Method for Instantiating Microstructure from Two-Point Statistics

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  15. Machine Learning-Based Prediction for Time Series Damage Evolution of Ni-Based Superalloy Microstructures

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  16. Machine Learning-Based Prediction for Time Series Damage Evolution of Ni-Based Superalloy Microstructures

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  17. Structure-property modeling scheme based on optimized microstructural information by two-point statistics and principal component analysis

    Journal of Materials Informatics · 2022-01-01

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  18. Hardness prediction of high entropy alloys with machine learning and material descriptors selection by improved genetic algorithm

    Computational Materials Science · 2022-01-18

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  19. Global‐Oriented Strategy for Searching Ultrastrength Martensitic Stainless Steels

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  20. Design of a NiTiHf shape memory alloy with an austenite finish temperature beyond 400 °C utilizing artificial intelligence

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  21. Shape memory elastomers: A review of synthesis, design, advanced manufacturing, and emerging applications

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  22. Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments

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  23. Continually reactivating iterative-projection method for instantiating microstructure from two-point statistics

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  24. Machine learning-based prediction for time series damage evolution of Ni-based superalloy microstructures

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  25. Machine learning-based inverse design for single-phase high entropy alloys

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  31. A Genetic Algorithm-Optimized Neural Network for Chlorophyll a Estimation Using MODIS Satellite Data in Coastal Water: Application to the Sinpho Bay of DPR Korea

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  32. Case Studies on Superalloy Design

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