Cover Image

Accelerate Microstructure Evolution Simulation Using Graph Neural Networks with Adaptive Spatiotemporal Resolution

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fan, Shaoxun, Hitt, Andrew L., Tang, Ming, Sadigh, Babak, Zhou, Fei
Format:	Preprint
Published:	2023
Subjects:	Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Monte Carlo Simulations of Crystal Defects in Open Ensembles
by: Walsh, Flynn, et al.
Published: (2025)

Ice phase classification made easy with score-based denoising
by: Sun, Hong, et al.
Published: (2024)

A probabilistic framework for crystal structure denoising, phase classification, and order parameters
by: Kwon, Hyuna, et al.
Published: (2025)

Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
by: Schwalbe-Koda, Daniel, et al.
Published: (2024)

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks
by: Fu, Li, et al.
Published: (2026)

Accelerating Discovery of Extreme Lattice Thermal Conductivity by Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding Intuitive Descriptors
by: Al-Fahdi, Mohammed, et al.
Published: (2024)

Efficient method for calculation of low-temperature phase boundaries
by: Svensson, Lucas, et al.
Published: (2026)

Grand canonical generative diffusion model for crystalline phases and grain boundaries
by: Lei, Bo, et al.
Published: (2024)

Scaling Kinetic Monte-Carlo Simulations of Grain Growth with Combined Convolutional and Graph Neural Networks
by: Tian, Zhihui, et al.
Published: (2025)

Stable Long-Horizon Spatiotemporal Prediction on Meshes Using Latent Multiscale Recurrent Graph Neural Networks
by: Salesses, Lionel, et al.
Published: (2026)

Equivalence of charged and neutral density functional formulations for correcting the many-body self-interaction of polarons
by: Falletta, Stefano, et al.
Published: (2025)

CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
by: Wander, Brook, et al.
Published: (2024)

Uncertainty Assessment of Probabilistic Cellular Automata Simulations in Microstructure Evolution
by: Seyed-Salehi, Majid
Published: (2024)

Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
by: Park, Yutack, et al.
Published: (2024)

Scalable Autoregressive Deep Surrogates for Dendritic Microstructure Dynamics
by: Ji, Kaihua, et al.
Published: (2025)

Neural Network Modeling of Microstructure Complexity Using Digital Libraries
by: Zhao, Yingjie, et al.
Published: (2025)

Hydrogen-Mediated Control of Phase Formation and Microstructure Evolution
by: Schweiger, Lukas, et al.
Published: (2025)

Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision
by: Ter-Petrosyan, Arman, et al.
Published: (2024)

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study
by: Ahmad, Owais, et al.
Published: (2025)

Mapping of Microstructure Transitions during Rapid Alloy Solidification Using Bayesian-Guided Phase-Field Simulations
by: Mancias, José, et al.
Published: (2025)

Extending Hamiltonian-Adaptive Resolution Simulation to Interfaces: An Updated LAMMPS Implementation and Application to Porous Solids
by: Sudhakar, Hari Haran, et al.
Published: (2026)

Accurate Prediction of Tensorial Spectra Using Equivariant Graph Neural Network
by: Hsu, Ting-Wei, et al.
Published: (2025)

Capabilities of Auto-encoders and Principal Component Analysis of the Reduction of Microstructural Images; Application on the Acceleration of Phase-Field Simulations
by: Fetni, Seifallah, et al.
Published: (2026)

Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins
by: Robertson, Andreas E., et al.
Published: (2025)

Using Thermodynamics and Microstructure to Mitigate Overfitting in Pellet Reduction Models
by: Büyükuslu, Ömer K., et al.
Published: (2025)

Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration
by: Mason, Simon A., et al.
Published: (2025)

Microstructural and Micromechanical Evolution of Olivine Aggregates During Transient Creep
by: Wiesman, Harison S., et al.
Published: (2024)

Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks
by: Elrashidy, Ahmed, et al.
Published: (2023)

Physics Informed Neural Network Enhanced Denoising for Atomic Resolution STEM Imaging
by: Awan, Z., et al.
Published: (2024)

Percolation-Driven Magnetotransport due to Structural and Microstructural Evolution in Ultrathin Si/Fe Bilayers
by: Das, S. S., et al.
Published: (2026)

Lagrangian Neural Networks for Reversible Dissipative Evolution
by: Sundararaghavan, Veera, et al.
Published: (2024)

Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations
by: Matsumura, Naoki, et al.
Published: (2025)

Co-Evolution-Based Metal-Binding Residue Prediction with Graph Neural Networks
by: Rastegari, Sayedmohammadreza, et al.
Published: (2025)

Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations
by: Störmer, Ian, et al.
Published: (2026)

Thermal and Microstructural Simulations of Photonic Sintering of Oxide Ceramics: A Two-Scale Scheme
by: Ma, Junlong, et al.
Published: (2026)

Inverse Design of Promising Alloys for Electrocatalytic CO$_2$ Reduction via Generative Graph Neural Networks Combined with Bird Swarm Algorithm
by: Song, Zhilong, et al.
Published: (2024)

Microstructural Evolution and Crystallization Behavior of Amorphous Medium-Entropy Ti-Nb-Zr-Ag Thin Films
by: Benediktová, Anna, et al.
Published: (2026)

Sustainable Pre-reduction of Ferromanganese Oxides with Hydrogen: Heating Rate-Dependent Reduction Pathways and Microstructure Evolution
by: Bajpai, Anurag, et al.
Published: (2025)

Equivariant Neural Network Force Fields for Magnetic Materials
by: Yuan, Zilong, et al.
Published: (2024)

Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks
by: Sun, Yixuan, et al.
Published: (2020)