Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks

• URL: http://arxiv.org/abs/2503.23794v1
• Date: Mon, 31 Mar 2025 07:14:32 GMT
• Title: Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks
• Authors: Fabian L. Thiemann, Thiago Reschützegger, Massimiliano Esposito, Tseden Taddese, Juan D. Olarte-Plata, Fausto Martelli,
• Abstract summary: We introduce TrajCast, a transferable and data-efficient framework based on autoregressive message passing networks.<n>We benchmark our framework across various systems, including a small molecule, crystalline material, and bulk liquid.<n>Depending on the system, TrajCast allows for forecast intervals up to $30times$ larger than traditional MD time-steps, generating over 15 ns of trajectory data per day for a solid with more than 4,000 atoms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Molecular dynamics (MD) simulations play a crucial role in scientific research. Yet their computational cost often limits the timescales and system sizes that can be explored. Most data-driven efforts have been focused on reducing the computational cost of accurate interatomic forces required for solving the equations of motion. Despite their success, however, these machine learning interatomic potentials (MLIPs) are still bound to small time-steps. In this work, we introduce TrajCast, a transferable and data-efficient framework based on autoregressive equivariant message passing networks that directly updates atomic positions and velocities lifting the constraints imposed by traditional numerical integration. We benchmark our framework across various systems, including a small molecule, crystalline material, and bulk liquid, demonstrating excellent agreement with reference MD simulations for structural, dynamical, and energetic properties. Depending on the system, TrajCast allows for forecast intervals up to $30\times$ larger than traditional MD time-steps, generating over 15 ns of trajectory data per day for a solid with more than 4,000 atoms. By enabling efficient large-scale simulations over extended timescales, TrajCast can accelerate materials discovery and explore physical phenomena beyond the reach of traditional simulations and experiments. An open-source implementation of TrajCast is accessible under https://github.com/IBM/trajcast.

Related papers

• Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science [0.0]
  Machine-learned interatomic potentials offer near first-principles accuracy but are computationally expensive. We present ML-mix, an efficient and flexible LAMMPS package for accelerating simulations by spatially mixing interatomic potentials. We show it is possible to generate a 'cheap' approximate model which closely matches an 'expensive' reference in relevant regions of configuration space.
  arXiv  Detail & Related papers  (2025-02-26T12:19:39Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Continual learning autoencoder training for a particle-in-cell simulation via streaming [52.77024349608834]
  upcoming exascale era will provide a new generation of physics simulations with high resolution. These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
  arXiv  Detail & Related papers  (2022-11-09T09:55:14Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks [4.444748822792469]
  Learning-based force fields have made significant progress in accelerating ab-initio MD simulation but are not fast enough for many real-world applications. We aim to address these challenges by learning a multi-scale graph neural network that directly simulates coarse-grained MD with a very large time step.
  arXiv  Detail & Related papers  (2022-04-21T18:07:08Z)
• Graph Neural Networks Accelerated Molecular Dynamics [0.0]
  We developed a GNN Accelerated Molecular Dynamics (GAMD) model that achieves fast and accurate force predictions. Our results show that GAMD can accurately predict the dynamics of two typical molecular systems, Lennard-Jones (LJ) particles and Water (LJ+Electrostatics)
  arXiv  Detail & Related papers  (2021-12-06T22:11:00Z)
• Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks [14.388196138756195]
  We introduce a graph neural network (GNN) based model, MDNet, to predict the evolution of coordinates and momentum with large time steps. We demonstrate the performance of MDNet on a 4000-atom system with large time steps, and show that MDNet can predict good equilibrium and transport properties.
  arXiv  Detail & Related papers  (2021-11-30T07:32:39Z)
• ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations [86.41674945012369]
  We develop a scalable and expressive Graph Neural Networks model, ForceNet, to approximate atomic forces. Our proposed ForceNet is able to predict atomic forces more accurately than state-of-the-art physics-based GNNs.
  arXiv  Detail & Related papers  (2021-03-02T03:09:06Z)
• Alternating ConvLSTM: Learning Force Propagation with Alternate State Updates [29.011464047344614]
  We introduce the alternating convolutional Long Short-Term Memory (Alt-ConvLSTM) that models the force propagation mechanisms in a deformable object with near-uniform material properties. We demonstrate how this novel scheme imitates the alternate updates of the first and second-order terms in the forward method of numerical PDE solvers. We validate our Alt-ConvLSTM on human soft tissue simulation with thousands of particles and consistent body pose changes.
  arXiv  Detail & Related papers  (2020-06-14T06:43:33Z)
• Learning to Simulate Complex Physics with Graph Networks [68.43901833812448]
  We present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains. Our framework---which we term "Graph Network-based Simulators" (GNS)--represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing. Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time.
  arXiv  Detail & Related papers  (2020-02-21T16:44:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.