[HTML][HTML] State-of-the-Art Review of the Simulation of Dynamic Recrystallization
X Liu, J Zhu, Y He, H Jia, B Li, G Fang - Metals, 2024 - mdpi.com
The evolution of microstructures during the hot working of metallic materials determines their
workability and properties. Recrystallization is an important softening mechanism in material …
workability and properties. Recrystallization is an important softening mechanism in material …
Machine Learning in Soft Matter: From Simulations to Experiments
K Zhang, X Gong, Y Jiang - Advanced Functional Materials, 2024 - Wiley Online Library
Soft matter with diverse functionalities that are easily designable has fascinated tremendous
research interests in the past several decades. Nevertheless, the inherent confluence of time …
research interests in the past several decades. Nevertheless, the inherent confluence of time …
A deep learning-based crystal plasticity finite element model
This study presents an innovative deep learning-based surrogate model for the Crystal
Plasticity Finite Element (CPFE) method, fundamentally transforming the generation of …
Plasticity Finite Element (CPFE) method, fundamentally transforming the generation of …
Unsupervised learning and pattern recognition in alloy design
Machine learning has the potential to revolutionise alloy design by uncovering useful
patterns in complex datasets and supplementing human expertise and experience. This …
patterns in complex datasets and supplementing human expertise and experience. This …
Setting the standard for machine learning in phase field prediction: a benchmark dataset and baseline metrics
Phase field models are an important mesoscale method that serves as a bridge between the
atomic scale and the macroscale, used for modeling complex phenomena at the …
atomic scale and the macroscale, used for modeling complex phenomena at the …
Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning
A Linda, MF Akhtar, S Pathak, S Bhowmick - Physical Review B, 2024 - APS
Stacking fault energies (SFEs) are key parameters to understand the deformation
mechanisms in metals and alloys, and prior knowledge of SFEs from ab initio calculations is …
mechanisms in metals and alloys, and prior knowledge of SFEs from ab initio calculations is …
Time series forecasting of multiphase microstructure evolution using deep learning
Microstructure evolution, which plays a critical role in determining materials properties, is
commonly simulated by the high-fidelity but computationally expensive phase-field method …
commonly simulated by the high-fidelity but computationally expensive phase-field method …
Neural network-driven framework for efficient microstructural modeling of particle-enriched composites
Simulating materials with complex, variable microstructures like nanoparticle-enriched
matrices presents significant challenges for FEM, particularly in defining constitutive …
matrices presents significant challenges for FEM, particularly in defining constitutive …
Grain boundary grooving in thin film under the influence of an external magnetic field: A phase-field study
S Bandyopadhyay, S Bhowmick… - Computational Materials …, 2025 - Elsevier
Using a phase-field model, we study the surface diffusion-controlled grooving of a moving
grain boundary under the influence of an external magnetic field in thin films of a non …
grain boundary under the influence of an external magnetic field in thin films of a non …
Deep operator network surrogate for phase-field modeling of metal grain growth during solidification
A deep operator network (DeepONet) has been constructed that generates accurate
representations of phase-field model simulations for evolving two dimensional metal grain …
representations of phase-field model simulations for evolving two dimensional metal grain …