:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Störmer, Ian, Zavadlav, Julija
Format:	Preprint
Publié:	2026
Sujets:	Computational Physics Materials Science
Accès en ligne:	https://arxiv.org/abs/2603.24360
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
par: Röcken, Sebastien, et autres
Publié: (2025)

Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions
par: Fuchs, Paul, et autres
Publié: (2025)

Accurate machine learning force fields via experimental and simulation data fusion
par: Röcken, Sebastien, et autres
Publié: (2023)

chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
par: Fuchs, Paul, et autres
Publié: (2024)

Mapping Still Matters: Coarse-Graining with Machine Learning Potentials
par: Görlich, Franz, et autres
Publié: (2025)

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
par: Sanocki, Michal, et autres
Publié: (2025)

Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration
par: Fuchs, Paul, et autres
Publié: (2025)

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
par: Chen, Weilong, et autres
Publié: (2025)

Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys
par: Shuang, Fei, et autres
Publié: (2026)

Predicting solvation free energies with an implicit solvent machine learning potential
par: Röcken, Sebastien, et autres
Publié: (2024)

Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-scale Simulations
par: Matsumura, Naoki, et autres
Publié: (2024)

Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis
par: Wander, Brook, et autres
Publié: (2024)

Molecular Dynamics Simulations of SrTiO$_3$ with Oxygen Vacancies using Neural Network Potentials
par: Nishiguchi, Kazutaka, et autres
Publié: (2025)

Local Order Average-Atom Interatomic Potentials
par: Zeller, Chloe A., et autres
Publié: (2025)

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
par: Fuchs, Paul, et autres
Publié: (2025)

Graph Neural Network for Unified Electronic and Interatomic Potentials: Strain-tunable Electronic Structures in 2D Materials
par: Choi, Moon-ki, et autres
Publié: (2025)

Large-Scale, Long-Time Atomistic Simulations of Proton Transport in Polymer Electrolyte Membranes Using a Neural Network Interatomic Potential
par: Yoshimoto, Yuta, et autres
Publié: (2025)

Phase-field model of alloy solidification far from chemical equilibrium at the solid-liquid interface
par: Ji, Kaihua, et autres
Publié: (2024)

The size effect on nucleation process during solidification of metals
par: Nuradinov, A. S., et autres
Publié: (2025)

Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
par: Chen, Siqi, et autres
Publié: (2024)

On-the-Fly Fine-Tuning of Foundational Neural Network Potentials: A Bayesian Neural Network Approach
par: Rensmeyer, Tim, et autres
Publié: (2025)

Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals
par: Heilman, Alex, et autres
Publié: (2024)

CTGNN: Crystal Transformer Graph Neural Network for Crystal Material Property Prediction
par: Du, Zijian, et autres
Publié: (2024)

Resolving thermal gradients and solidification velocities during laser melting of a refractory alloy
par: Park, Hyunggon, et autres
Publié: (2024)

Efficient Crystal Structure Prediction Using Universal Neural Network Potential with Diversity Preservation in Genetic Algorithms
par: Shibayama, Takuya, et autres
Publié: (2025)

Investigating Material Interface Diffusion Phenomena through Graph Neural Networks in Applied Materials
par: Zhao, Zirui, et autres
Publié: (2024)

Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling
par: Costa, Nuno, et autres
Publié: (2025)

PINNs-MPF: A Physics-Informed Neural Network Framework for Multi-Phase-Field Simulation of Interface Dynamics
par: Elfetni, Seifallah, et autres
Publié: (2024)

Self-supervised Representations and Node Embedding Graph Neural Networks for Accurate and Multi-scale Analysis of Materials
par: Kong, Jian-Gang, et autres
Publié: (2022)

Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides
par: Neubert, Marlen, et autres
Publié: (2025)

Aluminum oxide coatings on Co-rich cathodes and interactions with organic electrolyte
par: Peiris, M. D. Hashan C., et autres
Publié: (2025)

Physics-Grounded Understanding of Thermal Boundary Conductance between Ga$_2$O$_3$ and SiC from a Feedforward Neural Network Potential
par: Liu, Nuohao, et autres
Publié: (2026)

Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2
par: Faiyad, Abrar, et autres
Publié: (2025)

Breaking the Precision Ceiling in Physics-Informed Neural Networks: A Hybrid Fourier-Neural Architecture for Ultra-High Accuracy
par: Lee, Wei Shan, et autres
Publié: (2025)

Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys
par: Kotama, Takanori, et autres
Publié: (2026)

Particle Trajectory Prediction in Discrete Element Simulations using a Graph-Based Interaction-Aware Model
par: Setty, Abhishek, et autres
Publié: (2025)

Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials
par: Ko, Tsz Wai, et autres
Publié: (2024)

Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations
par: Matsumura, Naoki, et autres
Publié: (2025)

Asymptotic Analysis of the Pauli Potential for Atoms
par: Redd, Jeremy J., et autres
Publié: (2021)

Precipitate-Induced Dynamic Strain Aging and Its Effect on the Strain Rate Sensitivity of Precipitation Hardened Aluminum Alloys
par: Choukir, Sahar, et autres
Publié: (2026)