Cover Image

Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cattin, Côme, Plé, Thomas, Adjoua, Olivier, Gouraud, Nicolaï, Lagardère, Louis, Piquemal, Jean-Philip
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Velocity Jumps for Molecular Dynamics
by: Gouraud, Nicolaï, 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)

Force-Field-Enhanced Neural Network Interactions: from Local Equivariant Embedding to Atom-in-Molecule properties and long-range effects
by: Plé, Thomas, et al.
Published: (2023)

Routine Molecular Dynamics Simulations Including Nuclear Quantum Effects: from Force Fields to Machine Learning Potentials
by: Plé, Thomas, et al.
Published: (2022)

Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects
by: Inizan, Théo Jaffrelot, et al.
Published: (2022)

The Q-AMOEBA (CF) Polarizable Potential
by: Mauger, Nastasia, et al.
Published: (2025)

Lambda-ABF: Simplified, Portable, Accurate and Cost-effective Alchemical Free Energy Computations
by: Lagardère, Louis, et al.
Published: (2023)

The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery
by: Ansari, Narjes, et al.
Published: (2026)

Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals
by: Benali, Anouar, et al.
Published: (2025)

Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations
by: Ansari, Narjes, et al.
Published: (2025)

Non-Iterative Disentangled Unitary Coupled-Cluster based on Lie-algebraic structure
by: Haidar, Mohammad, et al.
Published: (2024)

High‐Resolution Molecular‐Dynamics Simulations of the Pyruvate Kinase Muscle Isoform 1 and 2 (PKM1/2)
by: Quentin Delobelle, et al.
Published: (2025)

High Performance Quantum Emulation for Chemistry Applications with Hyperion
by: Adjoua, Olivier, et al.
Published: (2026)

Greedy Gradient-free Adaptive Variational Quantum Algorithms on a Noisy Intermediate Scale Quantum Computer
by: Feniou, César, et al.
Published: (2023)

Raising the Performance of the Tinker-HP Molecular Modeling Package [Article v1.0]
by: Jolly, Luc-Henri, et al.
Published: (2019)

ReaxFF Simulations of Self-Assembled Monolayers On Silver Surfaces and Nanocrystals
by: Lahouari, Adam, et al.
Published: (2023)

Lambda-ABF-OPES: Faster Convergence with High Accuracy in Alchemical Free Energy Calculations
by: Ansari, Narjes, et al.
Published: (2025)

Leveraging Analog Quantum Computing with Neutral Atoms for Solvent Configuration Prediction in Drug Discovery
by: D'Arcangelo, Mauro, et al.
Published: (2023)

Sparse Quantum State Preparation for Strongly Correlated Systems
by: Feniou, C., et al.
Published: (2023)

Shortcut to Chemically Accurate Quantum Computing via Density-based Basis-set Correction
by: Traore, Diata, et al.
Published: (2024)

Simulating Electron Dynamics with GPU-Accelerated Real-Time Tamm-Dancoff Approximation
by: Knoll, Thomas, et al.
Published: (2026)

Open Source Variational Quantum Eigensolver Extension of the Quantum Learning Machine (QLM) for Quantum Chemistry
by: Haidar, Mohammad, et al.
Published: (2022)

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

Quantum Circuits for the Metropolis-Hastings Algorithm
by: Claudon, Baptiste, et al.
Published: (2025)

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

Real-Space Chemistry on Quantum Computers: A Fault-Tolerant Algorithm with Adaptive Grids and Transcorrelated Extension
by: Feniou, César, et al.
Published: (2025)

Practical protein-pocket hydration-site prediction for drug discovery on a quantum computer
by: Loco, Daniele, et al.
Published: (2025)

MixPI: Mixed-Time Slicing Path Integral Software for Quantized Molecular Dynamics Simulations
by: Johnson, Britta A., et al.
Published: (2024)

NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks
by: Tajs, Patryk, et al.
Published: (2025)

A Neural-Network-Based Selective Configuration Interaction Approach to Molecular Electronic Structure
by: Schmerwitz, Yorick L. A., et al.
Published: (2024)

RNA Dynamics and Interactions Revealed through Atomistic Simulations
by: Languin-Cattoën, Olivier, et al.
Published: (2025)

An Automated Framework for Analyzing Structural Evolution in On-the-fly Non-adiabatic Molecular Dynamics Using Autoencoder and Multiple Molecular Descriptors
by: Liu, Hangxu, et al.
Published: (2025)

Resonant Coupling Between Electromagnetic Waves and Protein Conformational Dynamics Revealed by Molecular Dynamics Simulations
by: Chen, Jiafei, et al.
Published: (2026)

Simulation of Pump-Push Molecular Dynamics in the Heptazine-H2O Complex
by: Pios, Sebastian V., et al.
Published: (2025)

Improved Treatment of 1-4 interactions in Force Fields for Molecular Dynamics Simulations
by: Abdullah, Aalim S., et al.
Published: (2025)

Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics
by: Xia, Junfan, et al.
Published: (2025)

Effect of Dispersity on Dynamic Properties of Polymer Melts: Insights from Coarse-Grained Molecular Dynamics Simulations
by: Tejuosho, Taofeek, et al.
Published: (2024)

Accelerating Molecular Dynamics through Informed Resetting
by: Church, Jonathan R., et al.
Published: (2024)

Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
by: Eastman, Peter, et al.
Published: (2026)