:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Garanger, Kévin, Kraus, Julie, Rimoli, Julian J.
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Materials Science
Accesso online:	https://arxiv.org/abs/2312.13511
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Atrial constitutive neural networks
di: Peirlinck, Mathias, et al.
Pubblicazione: (2025)

Leveraging neural network interatomic potentials for a foundation model of chemistry
di: Kim, So Yeon, et al.
Pubblicazione: (2025)

Advancing atomic electron tomography with neural networks
di: Lee, Juhyeok, et al.
Pubblicazione: (2025)

Concurrent enforcement of polyconvexity and true-stress-true-strain monotonicity in incompressible isotropic hyperelasticity: application to neural network constitutive models
di: Wollner, Maximilian P., et al.
Pubblicazione: (2026)

Invariant multiscale neural networks for data-scarce scientific applications
di: Schurov, I., et al.
Pubblicazione: (2024)

Recurrent neural networks and transfer learning for elasto-plasticity in woven composites
di: Ghane, Ehsan, et al.
Pubblicazione: (2023)

Differentiable neural network representation of multi-well, locally-convex potentials
di: Jones, Reese E., et al.
Pubblicazione: (2025)

Consistent machine learning for topology optimization with microstructure-dependent neural network material models
di: Vijayakumaran, Harikrishnan, et al.
Pubblicazione: (2024)

A comparative study of transformer models and recurrent neural networks for path-dependent composite materials
di: Uvdal, Petter, et al.
Pubblicazione: (2026)

Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response
di: Patel, Ravi, et al.
Pubblicazione: (2024)

Contrast transfer functions help quantify neural network out-of-distribution generalization in HRTEM
di: DaCosta, Luis Rangel, et al.
Pubblicazione: (2025)

Bayesian neural networks for predicting uncertainty in full-field material response
di: Pasparakis, George D., et al.
Pubblicazione: (2024)

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

Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence
di: Christiansen, Mads-Peter Verner, et al.
Pubblicazione: (2025)

Structural and mechanical properties of W-Cu compounds characterized by a neural-network-based potential
di: Liu, Jianchuan, et al.
Pubblicazione: (2025)

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
di: Gouvêa, Rogério Almeida, et al.
Pubblicazione: (2025)

Discovering uncertainty: Gaussian constitutive neural networks with correlated weights
di: McCulloch, Jeremy A., et al.
Pubblicazione: (2025)

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

Harnessing uncertainty when learning through Equilibrium Propagation in neural networks
di: Peters, Jonathan, et al.
Pubblicazione: (2025)

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

Graph neural network framework for energy mapping of hybrid monte-carlo molecular dynamics simulations of Medium Entropy Alloys
di: Ehsan, Mashaekh Tausif, et al.
Pubblicazione: (2024)

Physics-informed neural network for predicting fatigue life of unirradiated and irradiated austenitic and ferritic/martensitic steels under reactor-relevant conditions
di: Kori, Dhiraj S, et al.
Pubblicazione: (2025)

Exploring structure diversity in atomic resolution microscopy with graph neural networks
di: Luo, Zheng, et al.
Pubblicazione: (2024)

Less can be more for predicting properties with large language models
di: Alampara, Nawaf, et al.
Pubblicazione: (2024)

Graph-neural-network predictions of solid-state NMR parameters from spherical tensor decomposition
di: Mahmoud, Chiheb Ben, et al.
Pubblicazione: (2024)

Symmetry-Aware Bayesian Flow Networks for Crystal Generation
di: Ruple, Laura, et al.
Pubblicazione: (2025)

Physically recurrent neural network for rate and path-dependent heterogeneous materials in a finite strain framework
di: Maia, M. A., et al.
Pubblicazione: (2024)

Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery
di: Xu, Hao, et al.
Pubblicazione: (2025)

Broken neural scaling laws in materials science
di: Großmann, Max, et al.
Pubblicazione: (2026)

SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
di: Levy, Daniel, et al.
Pubblicazione: (2025)

Physics-informed neural operator for predictive parametric phase-field modelling
di: Chen, Nanxi, et al.
Pubblicazione: (2026)

Symmetry-enforced Moiré Topology
di: Liu, Yunzhe, et al.
Pubblicazione: (2025)

Automating modeling in mechanics: LLMs as designers of physics-constrained neural networks for constitutive modeling of materials
di: Tacke, Marius, et al.
Pubblicazione: (2025)

Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks
di: Nguyen, Tri Minh, et al.
Pubblicazione: (2024)

Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks
di: Yanguas-Gil, Angel, et al.
Pubblicazione: (2022)

Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites
di: Petković, Marko, et al.
Pubblicazione: (2025)

Probing the limitations of multimodal language models for chemistry and materials research
di: Alampara, Nawaf, et al.
Pubblicazione: (2024)

Non-normal spectral signatures of instability in neural network training dynamics
di: Ghosh, Souvik
Pubblicazione: (2026)

Symmetry-enforced Fermi degeneracy in topological semimetal RhSb$_3$
di: Wang, Kefeng, et al.
Pubblicazione: (2017)

WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
di: Kelvinius, Filip Ekström, et al.
Pubblicazione: (2025)