Cover Image

Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Matsumura, Naoki, Yoshimoto, Yuta, Iwasaki, Yuto, Yamazaki, Meguru, Sakai, Yasufumi
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Materials Science Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Molecular Dynamics Simulations of SrTiO$_3$ with Oxygen Vacancies using Neural Network Potentials
by: Nishiguchi, Kazutaka, et al.
Published: (2025)

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

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

Transferability of the chemical bond-based machine learning model for dipole moment: the GHz to THz dielectric properties of liquid propylene glycol and polypropylene glycol
by: Amano, Tomohito, et al.
Published: (2024)

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)

Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
by: Shah, Karan, et al.
Published: (2024)

Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
by: Park, Yutack, et al.
Published: (2024)

Molecular Dynamics Simulations of $γ$-Belite(010)-Water Interfaces with High-Dimensional Neural Network Potentials
by: Prus, Bernadeta, et al.
Published: (2025)

Do Graph Neural Networks Work for High Entropy Alloys?
by: Zhang, Hengrui, et al.
Published: (2024)

Kolmogorov-Arnold Neural Networks for High-Entropy Alloys Design
by: Bandyopadhyay, Yagnik, et al.
Published: (2024)

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

Scaling Kinetic Monte-Carlo Simulations of Grain Growth with Combined Convolutional and Graph Neural Networks
by: Tian, Zhihui, et al.
Published: (2025)

On-Chip Learning with Memristor-Based Neural Networks: Assessing Accuracy and Efficiency Under Device Variations, Conductance Errors, and Input Noise
by: Eslami, M. Reza, et al.
Published: (2024)

Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
by: Polat, Can, et al.
Published: (2025)

Molecular Dynamics Simulations of Anisotropic Particles Accelerated by Neural-Net Predicted Interactions
by: Argun, B. Rusen, et al.
Published: (2024)

Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers
by: S., Sindu B., et al.
Published: (2026)

Advancing Nonadiabatic Molecular Dynamics Simulations for Solids: Achieving Supreme Accuracy and Efficiency with Machine Learning
by: Zhang, Changwei, et al.
Published: (2024)

Accelerate Microstructure Evolution Simulation Using Graph Neural Networks with Adaptive Spatiotemporal Resolution
by: Fan, Shaoxun, et al.
Published: (2023)

Isotropic Shrinkage of Injection‐Molded Plates of Polypropylene Containing Calcium Salt of 4‐Methyl‐Cyclohexane‐1,2‐Dicarboxylic Acid
by: Takahiro Inoue, et al.
Published: (2025)

MolLIBRA: Genetic Molecular Optimization with Multi-Fingerprint Surrogates and Text-Molecule Aligned Critic
by: Okada, Masahi, et al.
Published: (2026)

Capabilities of Auto-encoders and Principal Component Analysis of the Reduction of Microstructural Images; Application on the Acceleration of Phase-Field Simulations
by: Fetni, Seifallah, et al.
Published: (2026)

Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
by: Nam, Juno, et al.
Published: (2024)

A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy
by: Wada, Naoki, et al.
Published: (2026)

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
by: Schönbauer, Sita, et al.
Published: (2025)

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

Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science
by: Chen, An, et al.
Published: (2024)

Lagrangian Neural Networks for Reversible Dissipative Evolution
by: Sundararaghavan, Veera, et al.
Published: (2024)

Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks
by: Azzaz, Riadh, et al.
Published: (2024)

Orb: A Fast, Scalable Neural Network Potential
by: Neumann, Mark, et al.
Published: (2024)

Toward Generalizable Surrogate Models for Molecular Dynamics via Graph Neural Networks
by: Immanuel, Judah, et al.
Published: (2025)

Atomistic Simulation Guided Convolutional Neural Networks for Thermal Modeling of Friction Stir Welding
by: Mishra, Akshansh
Published: (2025)

A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints
by: Fujii, Akihiro, et al.
Published: (2024)

Accelerating Quantum Emitter Characterization with Latent Neural Ordinary Differential Equations
by: Proppe, Andrew H., et al.
Published: (2024)

Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductors
by: Gu, Qiangqiang, et al.
Published: (2025)

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

MolMiner: Towards Controllable, 3D-Aware, Fragment-Based Molecular Design
by: Ortega-Ochoa, Raul, et al.
Published: (2024)

Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation
by: Huang, Kevin Han, et al.
Published: (2025)

Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
by: Jia, Shuyi, et al.
Published: (2025)

Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy
by: Chotrattanapituk, Abhijatmedhi, et al.
Published: (2026)

Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites
by: Petković, Marko, et al.
Published: (2024)