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RESEARCH PAPER · npj Computational Materials · 2020

Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus

Fei Yang, Zhen Li, Qing Wang, Beibei Jiang, Biaojie Yan, Pengcheng Zhang, Wei Xu, Chuang Dong, Peter Kaehuei Liaw

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Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulusOriginalA review on alloy design, biological response, and strengthening of β-titanium alloys as biomaterials357Composition rules of Ni-base single crystal superalloys and its influence on creep properties via a cluster formula approach35Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield97Machine Learning for High-Entropy Alloys10A CALPHAD-type Young's modulus database of Ti-rich Ti–Nb–Zr–Mo system20High-throughput determination of the composition-dependent mechanical and diffusion properties in β Ti–Nb–Zr–Hf refractory alloys29A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys47Applications of artificial intelligence and machine learning in metal additive manufacturing62Developing an XGBoost Regression Model for Predicting Young’s Modulus of Intact Sedimentary Rocks for the Stability of Surface and Subsurface Structures93Well-dispersed Li2CoTi3O8 nanoparticles as a multifunctional material for lithium-ion batteries and lithium-sulfur batteries24XGBoost automates the characterisation of reversibly actuating planar-flow-casted NiTi shape memory alloy foil1Computational intelligence based design of biomaterials15Construction of the Integrated “Material Design Theory and Application” Course Based on Academic Innovation0Machine-learning and high-throughput studies for high-entropy materials165Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield49New β-type Ti-Zr-V-Nb alloys used for laser-based direct energy deposition: Design, microstructure, and properties14Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys21Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures21Machine-learning-assisted discovery of empirical rule for inherent brittleness of full Heusler alloys15Material machine learning for alloys: Applications, challenges and perspectives150Artificial intelligence automates the characterization of reversibly actuating planar-flow-casted NiTi shape memory alloy foil6Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength56Gaussian process regressions on hot deformation behaviors of FGH98 nickel-based powder superalloy48Multi-objective optimization for high-performance Fe-based metallic glasses via machine learning approach15Novel Biomedical Ti-Based Alloys with Low Young’s Modulus: A First-Principles Study3Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys8NiTiHfX Shape Memory Alloy Actuation Strain Prediction1High-throughput exploration of composition-dependent elasto-plastic and diffusion properties of refractory multi-element Ti-Nb-Zr-W alloys8Recent progress in gradient-structured metals and alloys313Integrating machine learning and CALPHAD method for exploring low‐modulus near‐β‐Ti alloys47An embedded machine learning strategy for analyzing interfacial characteristics in impact welding of dissimilar alloys6A Review of Deformation Mechanisms, Compositional Design, and Development of Titanium Alloys with Transformation-Induced Plasticity and Twinning-Induced Plasticity Effects66Hybrid PSO with tree-based models for predicting uniaxial compressive strength and elastic modulus of rock samples24Machine learning-assisted design of low elastic modulus β-type medical titanium alloys and experimental validation27Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature16Design of high performance Cu-Ni-Si alloys via a multiobjective strategy based on machine learning12Experimentally validated inverse design of multi-property Fe-Co-Ni alloys30Machine learning-enabled prediction of high-temperature oxidation resistance for Ni-based alloys29Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization28Predictive modeling for design of low elastic modulus and biocompatible β-Ti alloys: A comprehensive machine learning-data driven approach with XGBoost as the key predictor8Predicting actuation strain in quaternary shape memory alloy NiTiHfX using machine learning11Applications of machine learning method in high-performance materials design: a review35High-throughput studies and machine learning for design of β titanium alloys with optimum properties12Comprehensive review of biological response, alloy design, strengthening mechanisms, performance evaluation, and surface modifications of titanium alloys for biomedical applications15Modeling and Prediction Method for Young’s Moduli of Ti Alloys Based on Residual Muti-layer Perceptron8Predicting and Enhancing Ductility in X2YZ-type Heusler Alloys Using Machine Learning and Abinitio Calculations0Physics-coupled data-driven design of high-temperature alloys12Robustness of machine learning predictions for Fe-Co-Ni alloys prepared by various synthesis methods8Developing novel low-density high-entropy superalloys with high strength and superior creep resistance guided by automated machine learning43Machine learning-based prediction of the mechanical properties of β titanium shape memory alloys22Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning9Machine learning in additive manufacturing: enhancing design, manufacturing and performance prediction intelligence22Integrating Machine Learning into Additive Manufacturing of Metallic Biomaterials: A Comprehensive Review20Predicting Elastic Modulus of Rocks Using Metaheuristic-Optimized Ensemble Regression Models5Application of Machine Learning in Amorphous Alloys4Integration of machine learning with finite element analysis in materials science: a review10Machine learning for obtaining values of thermomechanical processing parameters of high-entropy alloys with desirable strength and ductility13Generalizable descriptors for automatic titanium alloys design by learning from texts via large language model21Computation- and process-based design for advanced structural high-entropy alloy development and analyses: A critical review32Graph neural networks enable design of high-performance aluminum alloys for laser powder bed fusion2Review: Recent progress in titanium alloys development via machine learning and high-throughput preparation4Soft computing-based modelling of mechanical properties of titanium alloys1Recent advances in the high entropy materials for advanced energy storage with machine learning6Elucidating the intrinsic composition-microstructure-property relationships of Cu-Ni-Sn alloys by integrating rapid fabrication, thermodynamic calculations, and machine learning2Machine learning assisted optimization design and microstructure-property evolution mechanisms of 600 °C high temperature burn resistant titanium alloys7Record-low elastic modulus in β -titanium alloys designed using a domain adversarial neural network2Exploring the ductility of refractory high-entropy alloys: A parallel machine learning framework with experiments3Machine learning-aided design of metallic mechanical metamaterials: fabrication-aware modelling, property prediction, and inverse design9Machine learning-aided material selection, parameter optimization, and process monitoring in laser powder bed fusion and laser directed energy deposition with powder feed0Machine‑learning prediction of Young’s modulus in multi‑component titanium‑based biomedical alloys using extended thermodynamic descriptors0The validation and verification of high-throughput equivalent ternary CALPHAD algorithm for thermodynamic properties of multicomponent Ti alloys0Machine learning for titanium alloy development: Composition design, process optimization, microstructure informatics, and performance prediction0Research progress on advanced titanium-molybdenum alloy design driven by machine learning0Design of metastable β-type Ti–Nb–Mo alloys with low elastic modulus for biomedical applications: Evaluation of phase stability, mechanical properties, and corrosion resistance0Cross-scale regulation mechanisms of alloying elements on the mechanical properties of β-Ti alloys: a synergistic strategy to integrate machine learning and first-principles calculations0Intelligent additive manufacturing using AI and machine learning0Machine learning for high-performance alloy design: A review0

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  5. A CALPHAD-type Young's modulus database of Ti-rich Ti–Nb–Zr–Mo system

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  6. High-throughput determination of the composition-dependent mechanical and diffusion properties in β Ti–Nb–Zr–Hf refractory alloys

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  7. 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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  9. Developing an XGBoost Regression Model for Predicting Young’s Modulus of Intact Sedimentary Rocks for the Stability of Surface and Subsurface Structures

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  10. Well-dispersed Li2CoTi3O8 nanoparticles as a multifunctional material for lithium-ion batteries and lithium-sulfur batteries

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  13. Construction of the Integrated “Material Design Theory and Application” Course Based on Academic Innovation

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  15. Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield

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  17. Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys

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  18. Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures

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  19. Machine-learning-assisted discovery of empirical rule for inherent brittleness of full Heusler alloys

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  21. Artificial intelligence automates the characterization of reversibly actuating planar-flow-casted NiTi shape memory alloy foil

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