Cover Image

Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Koker, Teddy, Quigley, Keegan, Taw, Eric, Tibbetts, Kevin, Li, Lin
Format:	Preprint
Published:	2023
Subjects:	Computational Physics Materials Science Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
by: Koker, Teddy, et al.
Published: (2026)

Training a Foundation Model for Materials on a Budget
by: Koker, Teddy, et al.
Published: (2025)

Equivariant Matrix Function Neural Networks
by: Batatia, Ilyes, et al.
Published: (2023)

FIRE-GNN: Force-informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties
by: Hsu, Circe, et al.
Published: (2025)

XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures
by: Grizzi, Vitor F., et al.
Published: (2026)

Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
by: Maruf, Moin Uddin, et al.
Published: (2025)

A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
by: Yan, Keqiang, et al.
Published: (2025)

Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction
by: Kaniselvan, Manasa, et al.
Published: (2025)

Clever Materials: When Models Identify Good Materials for the Wrong Reasons
by: Jablonka, Kevin Maik
Published: (2026)

Equivariant Diffusion for Crystal Structure Prediction
by: Lin, Peijia, et al.
Published: (2025)

Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
by: Peng, Jiayu, et al.
Published: (2024)

Crystal Structure Prediction by Joint Equivariant Diffusion
by: Jiao, Rui, et al.
Published: (2023)

Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks
by: Thiemann, Fabian L., et al.
Published: (2025)

A Message Passing Neural Network Surrogate Model for Bond-Associated Peridynamic Material Correspondence Formulation
by: Hu, Xuan, et al.
Published: (2024)

Hybrid Quantum Graph Neural Network for Molecular Property Prediction
by: Vitz, Michael, et al.
Published: (2024)

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
by: Li, Chaojian, et al.
Published: (2025)

Towards Harmonization of SO(3)-Equivariance and Expressiveness: a Hybrid Deep Learning Framework for Electronic-Structure Hamiltonian Prediction
by: Yin, Shi, et al.
Published: (2024)

Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals
by: Heilman, Alex, et al.
Published: (2024)

Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink
by: Elsborg, Jonas, et al.
Published: (2026)

CrysToGraph: A Comprehensive Predictive Model for Crystal Materials Properties and the Benchmark
by: Wang, Hongyi, et al.
Published: (2024)

Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules
by: Segal, Nofit, et al.
Published: (2025)

ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction
by: Huang, Jiao, et al.
Published: (2024)

Molecule Graph Networks with Many-body Equivariant Interactions
by: Mao, Zetian, et al.
Published: (2024)

Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
by: Lee, Wei Shan, 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)

Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory
by: Zhang, Xuan, et al.
Published: (2026)

ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities
by: Nguyen, Tri Minh, et al.
Published: (2026)

Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)
by: Gray, Zoë J., et al.
Published: (2025)

On-the-Fly Fine-Tuning of Foundational Neural Network Potentials: A Bayesian Neural Network Approach
by: Rensmeyer, Tim, et al.
Published: (2025)

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
by: Ullberg, R. Seaton, et al.
Published: (2026)

A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
by: Solé, Àlex, et al.
Published: (2025)

Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks
by: Li, Longze, et al.
Published: (2023)

Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
by: Jia, Shuyi, 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)

The Northeast Materials Database for Magnetic Materials
by: Itani, Suman, et al.
Published: (2024)

A General Neural Network Potential for Energetic Materials with C, H, N, and O elements
by: Wen, Mingjie, et al.
Published: (2025)

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

Breaking the Precision Ceiling in Physics-Informed Neural Networks: A Hybrid Fourier-Neural Architecture for Ultra-High Accuracy
by: Lee, Wei Shan, et al.
Published: (2025)

Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks
by: Song, Siyuan, et al.
Published: (2023)

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