Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space

• URL: http://arxiv.org/abs/2505.16301v1
• Date: Thu, 22 May 2025 06:56:19 GMT
• Title: Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space
• Authors: Fuchun Ge, Pavlo O. Dral,
• Abstract summary: We present MDtrajNet-1, a foundational AI model that directly generates MD trajectories across chemical space.<n>The model's flexible design supports diverse application scenarios, including different statistical ensembles, boundary conditions, and interaction types.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency. We present MDtrajNet-1, a foundational AI model that directly generates MD trajectories across chemical space, bypassing force calculations and integration. This approach accelerates simulations by up to two orders of magnitude compared to traditional MD, even those enhanced by machine-learning interatomic potentials. MDtrajNet-1 combines equivariant neural networks with a Transformer-based architecture to achieve strong accuracy and transferability in predicting long-time trajectories for both known and unseen systems. Remarkably, the errors of the trajectories generated by MDtrajNet-1 for various molecular systems are close to those of the conventional ab initio MD. The model's flexible design supports diverse application scenarios, including different statistical ensembles, boundary conditions, and interaction types. By overcoming the intrinsic speed barrier of conventional MD, MDtrajNet-1 opens new frontiers in efficient and scalable atomistic simulations.

Related papers

• Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
  We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
  arXiv  Detail & Related papers  (2025-05-25T01:27:18Z)
• Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks [0.0]
  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.
  arXiv  Detail & Related papers  (2025-03-31T07:14:32Z)
• Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning [52.28945097811129]
  We propose an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences.<n>We reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear.<n>Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources.
  arXiv  Detail & Related papers  (2024-10-08T14:27:28Z)
• 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)
• Neural Operators for Accelerating Scientific Simulations and Design [85.89660065887956]
  An AI framework, known as Neural Operators, presents a principled framework for learning mappings between functions defined on continuous domains. Neural Operators can augment or even replace existing simulators in many applications, such as computational fluid dynamics, weather forecasting, and material modeling.
  arXiv  Detail & Related papers  (2023-09-27T00:12:07Z)
• SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases [66.61789780666727]
  We show how the second-order continuity can be incorporated into GNNs while maintaining the equivariant property. We also offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states. Our model yields a significant improvement over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-08-25T07:15:58Z)
• On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver [59.13397937903832]
  We introduce a deep learning-based corrector called Neural Vector (NeurVec) NeurVec can compensate for integration errors and enable larger time step sizes in simulations. Our experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability.
  arXiv  Detail & Related papers  (2022-08-07T09:02:18Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• 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)

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.