Cover Image

Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Grega, Ivan, Batatia, Ilyes, Csányi, Gábor, Karlapati, Sri, Deshpande, Vikram S.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Neural rendering enables dynamic tomography
by: Grega, Ivan, et al.
Published: (2024)

Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
by: Baldwin, William J., et al.
Published: (2026)

Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs
by: Warford, Thomas, et al.
Published: (2026)

Zero Shot Molecular Generation via Similarity Kernels
by: Elijošius, Rokas, et al.
Published: (2024)

A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories
by: Grega, Ivan, et al.
Published: (2025)

Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic Perovskites
by: Karimitari, Nima, et al.
Published: (2024)

Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
by: Kaur, Harveen, et al.
Published: (2024)

Inverse designing metamaterials with programmable nonlinear functional responses in graph space
by: Maurizi, Marco, et al.
Published: (2024)

Facet: highly efficient E(3)-equivariant networks for interatomic potentials
by: Miklaucic, Nicholas, et al.
Published: (2025)

Deconstructing equivariant representations in molecular systems
by: Lee, Kin Long Kelvin, et al.
Published: (2024)

Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO
by: Sivaraman, Ganesh, et al.
Published: (2024)

Learning local equivariant representations for quantum operators
by: Zhouyin, Zhanghao, et al.
Published: (2024)

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

Machine learning Hubbard parameters with equivariant neural networks
by: Uhrin, Martin, et al.
Published: (2024)

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
by: Du, Hongwei, et al.
Published: (2025)

General time-reversal equivariant neural network potential for magnetic materials
by: Yu, Hongyu, et al.
Published: (2022)

Bond-Network Entropy Governs Heat Transport in Coordination-Disordered Solids
by: Iwanowski, Kamil, et al.
Published: (2024)

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials on organic and inorganic compounds
by: Laskaris, G., et al.
Published: (2026)

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

TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction
by: Yin, Shi, et al.
Published: (2024)

A practical guide to machine learning interatomic potentials -- Status and future
by: Jacobs, Ryan, et al.
Published: (2025)

Patterns of load, elastic energy and damage in network models of architected composite materials
by: Greff, Christian, et al.
Published: (2026)

Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning
by: Barad, Sagar Prakash, et al.
Published: (2024)

Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structures
by: Guo, Leo, et al.
Published: (2025)

Breaking better: How imperfections increase fracture resistance in architected lattices
by: Lingua, Alessandra, et al.
Published: (2025)

SHA-256 Infused Embedding-Driven Generative Modeling of High-Energy Molecules in Low-Data Regimes
by: Verma, Siddharth, et al.
Published: (2025)

Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloys
by: Cheula, Raffaele, et al.
Published: (2025)

Thermal Conductivity Predictions with Foundation Atomistic Models
by: Póta, Balázs, et al.
Published: (2024)

Graded anisotropic metamaterials for elastic wave mode conversion
by: Boddapati, Jagannadh, et al.
Published: (2026)

Learning local and semi-local density functionals from exact exchange-correlation potentials and energies
by: Kanungo, Bikash, et al.
Published: (2024)

An equivariant graph neural network for the elasticity tensors of all seven crystal systems
by: Wen, Mingjian, et al.
Published: (2023)

Resonances in reflective Hamiltonian Monte Carlo
by: Kroupa, Namu, et al.
Published: (2025)

Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network
by: Kiyani, Elham, et al.
Published: (2025)

FFT-based surrogate modeling of auxetic metamaterials with real-time prediction of effective elastic properties and swift inverse design
by: Danesh, Hooman, et al.
Published: (2024)

Energy-GNoME: A Living Database of Selected Materials for Energy Applications
by: De Angelis, Paolo, et al.
Published: (2024)

MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry
by: Batatia, Ilyes, et al.
Published: (2026)

Symmetry Breaking in the Superionic Phase of Silver-Iodide
by: Hajibabaei, Amir, et al.
Published: (2024)

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
by: Schaaf, Lars L., et al.
Published: (2024)

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials
by: Miret, Santiago, et al.
Published: (2025)