:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	DaCosta, Luis Rangel, Scott, Mary C.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Materials Science
Online Access:	https://arxiv.org/abs/2512.09067
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

Symmetry-enforcing neural networks with applications to constitutive modeling
by: Garanger, Kévin, et al.
Published: (2023)

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
by: Omee, Sadman Sadeed, et al.
Published: (2024)

Cross-functional transferability in universal machine learning interatomic potentials
by: Huang, Xu, et al.
Published: (2025)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

CbLDM: A Diffusion Model for recovering nanostructure from atomic pair distribution function
by: Cao, Jiarui, et al.
Published: (2025)

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

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

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

Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials
by: Georgaras, Johnathan D., et al.
Published: (2025)

Deep encoder-decoder hierarchical convolutional neural networks for conjugate heat transfer surrogate modeling
by: Ebbs-Picken, Takiah, et al.
Published: (2023)

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

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

Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science
by: Suzuki, Yuta, et al.
Published: (2025)

Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods
by: Barzdajn, Bartosz, et al.
Published: (2024)

Detective quantum efficiency based comparison of HRTEM and ptychography phase imaging
by: Bennemann, Felix, et al.
Published: (2025)

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

Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory
by: Manzhos, Sergei, et al.
Published: (2025)

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

Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints
by: Udofia, Benjamin, et al.
Published: (2024)

Out-of-distribution materials property prediction using adversarial learning based fine-tuning
by: Li, Qinyang, et al.
Published: (2024)

Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution
by: Cohn, Ryan, et al.
Published: (2021)

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

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