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RESEARCH PAPER · Materials & Design · 2019

Robust data-driven approach for predicting the configurational energy of high entropy alloys

Jiaxin Zhang, Xianglin Liu, Sirui Bi, Junqi Yin, Guannan Zhang, Markus Eisenbach

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Robust data-driven approach for predicting the configurational energy of high entropy alloysOriginalUltrastruktura komórek nabłonkowych kanalików krętych nerki po zatruciu insektycydem Sevin0Machine Learning the Effective Hamiltonian in High Entropy Alloys0Density functional simulations of pressurized Mg-Zn and Al-Zn alloys7New insights into ternary geometrical models for material design25Efficient construction of linear models in materials modeling and applications to force constant expansions60Modelling and prediction of hardness in multi-component alloys: A combined machine learning, first principles and experimental study68Titanium alloying enhancement of mechanical properties of NbTaMoW refractory high-Entropy alloy: First-principles and experiments perspective64Frontiers in atomistic simulations of high entropy alloys68Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach91Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics4Navigating the Complex Compositional Landscape of High-Entropy Alloys2Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey225Harnessing the Complex Compositional Space of High-Entropy Alloys9A general and transferable deep learning framework for predicting phase formation in materials93Predictive descriptors in machine learning and data-enabled explorations of high-entropy alloys73A tale of two phase diagrams: Interplay of ordering and hydrogen uptake in Pd–Au–H26Accelerated and conventional development of magnetic high entropy alloys207Transfer learning based variable-fidelity surrogate model for shell buckling prediction94Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications54Machine learning for alloys698Developing a multilateral-based neural network model for engineering of high entropy amorphous alloys8A Property‐Driven Stepwise Design Strategy for Multiple Low‐Melting Alloys via Machine Learning26Neural network-based order parameter for phase transitions and its applications in high-entropy alloys49Blackbox optimization for approximating high-fidelity heat transfer calculations in metal additive manufacturing3Machine learning-assisted design of biomedical high entropy alloys with low elastic modulus for orthopedic implants30Phase Prediction Study of High-Entropy Energy Alloy Generation Based on Machine Learning5Approaches for handling high-dimensional cluster expansions of ionic systems29Machine learning for high-entropy alloys: Progress, challenges and opportunities397Gradient-field pulsed eddy current imaging of hidden corrosion in layered conductors via Sparse Bayesian Learning alongside Baseline Estimation and Denoising with Sparsity8Machine learning accelerates the materials discovery142Exploiting Machine Learning in Multiscale Modelling of Materials12Element-wise representations with ECNet for material property prediction and applications in high-entropy alloys24Prediction of phase selection of amorphous alloys and high entropy alloys by artificial neural network19Simulating short-range order in compositionally complex materials84A map of single-phase high-entropy alloys173Transferable prediction of formation energy across lattices of increasing size1Transferable prediction of formation energy across lattices of increasing size1Deep learning modeling in microscopy imaging: A review of materials science applications62Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32Block sparsity promoting algorithm for efficient construction of cluster expansion models for multicomponent alloys3Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook78An experimental and theoretical framework for identifying the band type and the carrier transport properties of transition metal dichalcogenides heterostructures5Chemical Order/Disorder Phase Transitions in NiCoFeAlTiB High-Entropy Alloys: A Monte Carlo Analysis0Unsupervised learning and pattern recognition in alloy design15Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data23Mixed Enthalpy–Entropy Descriptor for the Rational Design of Synthesizable High-Entropy Materials Over Vast Chemical Spaces25Feature purify: An examination of spurious correlations in high-entropy alloys9Improving the mechanical properties of Cantor-like alloys with Bayesian optimization14Alloys innovation through machine learning: a statistical literature review14A multi-fidelity transfer learning strategy based on multi-channel fusion18A machine learning interatomic potential for high entropy alloys54Transferring predictions of formation energy across lattices of increasing size*4Chemistry and Local Environment Adaptive Representation graphs as material descriptors10Enhancing high-entropy alloy performance: Predictive modelling of wear rates with machine learning16Ab initio framework for deciphering trade-off relationships in multi-component alloys9VASE: A High-Entropy Alloy Short-Range Order Structural Descriptor for Machine Learning15Mapping high entropy state spaces for novel material discovery5Materials Properties Prediction (MAPP): Empowering the Prediction of Material Properties Solely Based on Chemical Formulas12Frontiers in high entropy alloys and high entropy functional materials169Machine learning-assisted design of high-entropy alloys for optimal strength and ductility23Chemical order/disorder phase transitions in NiCoFeAlTiB multi-principal element alloys: A Monte Carlo analysis4A harmonic domain regressor with dynamic task weighting strategy for multi-fidelity surrogate modeling in engineering design13Machine Learning Advances in High-Entropy Alloys: A Mini-Review25Solving the strength-ductility trade-off using complex concentrated alloy design strategy: An overview5Quantum annealing-assisted lattice optimization24A multi-fidelity transfer learning strategy for surface deformation measurement of large reflector antennas4EFTGAN: Elemental features and transferring corrected data augmentation for the study of high-entropy alloys20Machine Learning-Based Computational Design Methods for High-Entropy Alloys49Application of Machine Learning in Amorphous Alloys4Synthesis and Machine Learning Prediction of High Entropy Multi‐Principal Element Nanoparticles14PtWAVE: a high-sensitive deconvolution software of sequencing trace for the detection of large indels in genome editing1Language models for materials discovery and sustainability: Progress, challenges, and opportunities26Transferable predictions of energetic and structural properties for refractory solid solution alloys across chemical compositions2Data Science in Order and Disorder of High-Entropy Materials4Role of compositionally complex and high entropy alloys for hydrogen storage: A bibliometric and mechanistic assessment16Prediction of low-modulus Ti-based refractory high-entropy alloys for orthopedic implants using machine learning and DFT calculations9Characterization of Two Novel NiTiHf Shape Memory Alloys Designed by Machine Learning Utilizing Novel Experimental Techniques2Review: machine learning in high-entropy alloys-transformative potential and innovative application7Revealing nanostructures in high-entropy alloys via machine-learning accelerated scalable Monte Carlo simulation6Machine learning-based prediction of high-entropy alloys for hydrogen storage with optimized thermodynamic and kinetic parameters17SMC-X: A Distributed, Scalable Monte Carlo Simulation Method for Chemically Complex Alloys3A Comprehensive Review of the Computational Approaches for Structure–Property Prediction of High Entropy Materials0Competing adsorption of H and CO on Pd-alloy surfaces:Mechanistic insight into the mitigating effect of Cu on CO poisoning0Comparative assessment of dilute alloys and complex concentrated alloys: Design principles, processing, mechanisms, properties, and sustainability0Fine-tuning of universal machine-learning interatomic potentials for high-entropy alloys with application to 2D (Mo,Ta,Nb,W,V)S22Machine learning-driven accelerated discovery of high-entropy alloys: From composition engineering to multifunctional property prediction0Machine learning interatomic potentials for high-entropy alloys: from atomic mechanisms to macroscopic design0

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  1. Ultrastruktura komórek nabłonkowych kanalików krętych nerki po zatruciu insektycydem Sevin

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  2. Machine Learning the Effective Hamiltonian in High Entropy Alloys

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  3. Density functional simulations of pressurized Mg-Zn and Al-Zn alloys

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  4. New insights into ternary geometrical models for material design

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  5. Efficient construction of linear models in materials modeling and applications to force constant expansions

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  6. Modelling and prediction of hardness in multi-component alloys: A combined machine learning, first principles and experimental study

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  7. Titanium alloying enhancement of mechanical properties of NbTaMoW refractory high-Entropy alloy: First-principles and experiments perspective

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  8. Frontiers in atomistic simulations of high entropy alloys

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

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  10. Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics

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  11. Navigating the Complex Compositional Landscape of High-Entropy Alloys

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  12. Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey

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  13. Harnessing the Complex Compositional Space of High-Entropy Alloys

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  14. A general and transferable deep learning framework for predicting phase formation in materials

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  15. Predictive descriptors in machine learning and data-enabled explorations of high-entropy alloys

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  16. A tale of two phase diagrams: Interplay of ordering and hydrogen uptake in Pd–Au–H

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  17. Accelerated and conventional development of magnetic high entropy alloys

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  18. Transfer learning based variable-fidelity surrogate model for shell buckling prediction

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  19. Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications

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  20. Machine learning for alloys

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  21. Developing a multilateral-based neural network model for engineering of high entropy amorphous alloys

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  22. A Property‐Driven Stepwise Design Strategy for Multiple Low‐Melting Alloys via Machine Learning

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  23. Neural network-based order parameter for phase transitions and its applications in high-entropy alloys

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  25. Machine learning-assisted design of biomedical high entropy alloys with low elastic modulus for orthopedic implants

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  27. Approaches for handling high-dimensional cluster expansions of ionic systems

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  41. Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

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  47. Feature purify: An examination of spurious correlations in high-entropy alloys

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  48. Improving the mechanical properties of Cantor-like alloys with Bayesian optimization

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