Cover Image

Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xia, Junfan, Jiang, Bin
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Simple and Efficient Equivariant Message Passing Neural Network Model for Non-Local Potential Energy Surface
by: Wu, Yibin, et al.
Published: (2024)

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
by: Xia, Junfan, et al.
Published: (2025)

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces
by: Gouraud, Nicolaï, et al.
Published: (2026)

The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
by: Käser, Silvan, et al.
Published: (2024)

Efficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials
by: Marks, Jonah, et al.
Published: (2025)

Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes
by: Simeon, Guillem, et al.
Published: (2024)

FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
by: Plé, Thomas, et al.
Published: (2024)

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction
by: Ji, Shengjiao, et al.
Published: (2025)

Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics
by: Sun, Fang, et al.
Published: (2024)

Implicit Delta Learning of High Fidelity Neural Network Potentials
by: Thaler, Stephan, et al.
Published: (2024)

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
by: Pelaez, Raul P., et al.
Published: (2024)

Pretraining Strategy for Neural Potentials
by: Zhang, Zehua, et al.
Published: (2024)

Toward Routine CSP of Pharmaceuticals: A Fully Automated Protocol Using Neural Network Potentials
by: Glick, Zachary L., et al.
Published: (2025)

Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials
by: Zhang, Yaolong, et al.
Published: (2025)

Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model
by: Zhu, Jianshen, et al.
Published: (2025)

All-atomistic Transferable Neural Potentials for Protein Solvation
by: Dey, Rishabh, et al.
Published: (2026)

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
by: Jiang, Shengli, et al.
Published: (2023)

Molecular Learning Dynamics
by: Gusev, Yaroslav, et al.
Published: (2025)

$\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
by: Khrabrov, Kuzma, et al.
Published: (2024)

Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning
by: Liu, Gang, et al.
Published: (2024)

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials
by: Zariquiey, Francesc Sabanés, et al.
Published: (2025)

Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
by: Thiede, Luca, et al.
Published: (2026)

Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models
by: Arjona-Medina, Jose, et al.
Published: (2024)

Automated Molecular Concept Generation and Labeling with Large Language Models
by: Zhang, Zimin, et al.
Published: (2024)

Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
by: Cao, Zhonglin, et al.
Published: (2025)

Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics
by: Diez, Juan Viguera, et al.
Published: (2025)

Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generation
by: Zhu, Yifei, et al.
Published: (2025)

Operator Forces For Coarse-Grained Molecular Dynamics
by: Klein, Leon, et al.
Published: (2025)

Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model
by: Kim, Dongki, et al.
Published: (2025)

Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties
by: Maennel, Hartmut, et al.
Published: (2024)

Graph Neural Networks for Surfactant Multi-Property Prediction
by: Brozos, Christoforos, et al.
Published: (2024)

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

FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability
by: Panapitiya, Gihan, et al.
Published: (2024)

Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
by: Pavšek, Jan, et al.
Published: (2026)

Highly Accurate Real-space Electron Densities with Neural Networks
by: Cheng, Lixue, et al.
Published: (2024)

Open-Source Fermionic Neural Networks with Ionic Charge Initialization
by: Pranesh, Shai, et al.
Published: (2024)

Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation
by: Li, Yunyang, et al.
Published: (2023)

Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning
by: Zhang, He, et al.
Published: (2023)

Prediction of the Infrared Absorbance Intensities and Frequencies of Hydrocarbons:A Message Passing Neural Network Approach
by: Tameh, Maliheh Shaban, et al.
Published: (2024)

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
by: Chen, Weilong, et al.
Published: (2025)