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RESEARCH PAPER · Acta Materialia · 2022

Evolution analysis of γ' precipitate coarsening in Co-based superalloys using kinetic theory and machine learning

Pei Liu, Haiyou Huang, Xue Feng Jiang, Yan Zhang, Toshihiro Omori, Turab Lookman, Yanjing Su

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Evolution analysis of γ' precipitate coarsening in Co-based superalloys using kinetic theory and machine learningOriginalA novel neural network-based alloy design strategy: Gated recurrent unit machine learning modeling integrated with orthogonal experiment design and data augmentation50Machine Learning Captures Synthetic Intuitions for Hollow Nanostructures4Data-driven pitting evolution prediction for corrosion-resistant alloys by time-series analysis23Directional coalescence of γ' precipitates during long time aging of CoNiAlW superalloy9On real-time control of microstructure of TiAl specimens with varied cross-sections based on numerical calculation and machine learning3Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys17Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 202123Data-driven design of Ni-based turbine disc superalloys to improve yield strength56DRX mechanisms of a Ni-Co-W type superalloy with typical columnar grains during hot compression25Effect of temperature on tensile behavior, fracture morphology, and deformation mechanisms of Nickel-based additive manufacturing 939 superalloy54The γ/γ′ microstructure in CoNiAlCr-based superalloys using triple-objective optimization23A novel Co-Ni-Ti-V-based superalloy exhibiting low density and high strength14Data-driven design of brake pad composites for high-speed trains10Alloy synthesis and processing by semi-supervised text mining28Status and progress of metal-supported solid oxide fuel cell: Towards large-scale manufactory and practical applications65Mechanism of intermediate temperature abnormal plasticity in Co–Al–W-base superalloy8Pretraining on Matter: Conceptual Limits of Foundation Models for Materials Science0Predicting and understanding the ductility of BCC high entropy alloys via knowledge-integrated machine learning52Exploring the evolution of microstructure and mechanical property of a γʹ-strengthened Co-based single crystal superalloy during creep and thermal exposure at 1000 °C15Investigation of microstructural stability and tensile deformation behavior of a novel PM Ni-based superalloy during long-term thermal exposure44A review of the microstructure and properties of superalloys regulated by magnetic field27Coarsening transitional kinetics of γ′ precipitates in a nickel-based single crystal superalloy during thermal exposure41Instance segmentation from small dataset by a dual-layer semantics-based deep learning framework1Machine Learning Approach to Investigate High Temperature Corrosion of Critical Infrastructure Materials8Design and development of coating for metallic bipolar plates in proton exchange membrane fuel cell (PEMFC): A review56Identifying determinants of γ’ phase coarsening behavior in Co/CoNi-based superalloys with explainable artificial intelligence (XAI)4Multiexpression Symbolic Regression and Its Circuit Design Case2Construction of material phase diagram using different uncertainty estimation strategies2Multi-principal element alloy with ultrafine and high-density L12 nanoparticles7Unexpected Young's modulus dependence of refractory solute diffusion in NiCoFeCr-based high entropy alloys5Study of Microstructure and Mechanical Properties of 800H Alloy During Creep3Phase-field-informed machine learning on creep behavior of Ni-based single-crystal superalloys5The coarsening kinetics analysis of intermetallic γ′-precipitate evolution and the cyclic oxidation behaviour of (DS) CM 247 LC alloy0Dislocation motion and deformation mechanism of Ni-Co-Cr superalloys with intra-/intergranular γ′ precipitates and μ phases5Design of superalloys with multiple properties via multi-task learning8Designing nickel-based superalloys with high creep life and low thermal expansion coefficient using machine learning assisted by multi-objective optimization6Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning5Automated system for high-throughput process-structure-property dataset generation of structural materials: A γ/γ′ superalloy case study10Design of Novel High-Strength Ni-Based Superalloys Using a Data-Driven Approach1Physics-enhanced machine learning for predicting strength of high-carbon chromium steel during thermomechanical processing and spheroidizing annealing11A new creep prediction model in Ni‐based superalloys via data‐driven symbolic regression3Mapping microstructure to mechanical property by disentangling strengthening mechanism with deep learning11Enhancing Reverse Design Ability of Functional Materials Based on Data Quality Management: Taking Biomedical Zinc Alloy as an Example0Explainable machine learning-enabled dual-objective design of γ' phase characteristic parameters in γ'-strengthened Co-based superalloys11Predicting dislocation patterns and discovering the law of similitude: Machine learning based on fully reversed fatigue of FCC metals3Strength-ductility variation and boundary micro-mechanical property degradation induced by brittle μ phase in nickel-based superalloys2Coarsening Behaviors of γ′ Particles in a Co-Al-W-Based Single-Crystal Alloy0Rapid Discovery of High-Performance Additive Manufacturing Superalloys Using High-Throughput and Artificial Intelligence Methods0From microstructure to performance optimization: Innovative applications of computer vision in materials science13Microstructure and mechanical property evolution of GH4698 superalloy during long-term thermal exposure2Improving DFT-predicted band gaps by symbolic regression for semiconductor materials discovery5AI meets high-entropy alloys: From classification and regression to explainable models and predictive discovery1Experimental Data Integration into ML Workflows for Real Alloys: A Review of Noisy, Sparse, and Heterogeneous Fusion0Preparation, Characterization, and Application of Additive Manufacturing Superalloy Data0Dual engine-driven strategy for advanced copper alloy design employing large language models13Compositional design of Ni-based superalloys for additive manufacturing: Progress and perspectives9Machine learning-driven new paradigm for Co-based superalloys3Design of Fe-Co-Cr-Ni-Mn-Al-Ti Multi-Principal Element Alloys Based on Machine Learning0A comprehensive review of machine learning for superalloys: from data-driven prediction to intelligent design2The Mo effect in regulating γ′ phase stability and mechanical degradation of CoNi-based superalloys during thermal exposure2Identification and mechanistic understanding of key elements governing the thermal expansion coefficient in Ni-based superalloys: a combined machine learning and first principles study3Development of a novel γ–γ′ high-entropy superalloy: Microstructure and tensile properties0A novel γ–γ′ high-entropy superalloy with enhanced high-temperature performance0Recent advances in phase-field modeling of microstructural evolution in superalloys0Effect of Re and Mo on the microstructural evolution and hardness of CoNiAlCr-based superalloys at 1100 °C and 1150 °C0Thermodynamic calculation and solidification behavior in the Co−Mo−Ti system0Deep learning segmentation with quality control for γ/γ' microstructure quantitative characterization in Ni₃Al-based single-crystal superalloys0Microstructural evolution and mechanical degradation of a wrought Ni-based superalloy during long-term thermal exposure: insights into γ′ coalescence and grain-boundary degradation0

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