Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks

• URL: http://arxiv.org/abs/2201.01195v1
• Date: Sun, 2 Jan 2022 23:00:30 GMT
• Title: Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks
• Authors: Ludwig Winkler and Klaus-Robert M\"uller and Huziel E. Sauceda
• Abstract summary: We explore different machine learning (ML) methodologies to increase the resolution of molecular dynamics trajectories on-demand within a post-processing step. We have found that Bi-LSTMs are the best performing models; by utilizing the local time-symmetry of thermostated trajectories they can even learn long-range correlations and display high robustness to noisy dynamics across molecular complexity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular dynamics simulations are a cornerstone in science, allowing to investigate from the system's thermodynamics to analyse intricate molecular interactions. In general, to create extended molecular trajectories can be a computationally expensive process, for example, when running $ab-initio$ simulations. Hence, repeating such calculations to either obtain more accurate thermodynamics or to get a higher resolution in the dynamics generated by a fine-grained quantum interaction can be time- and computationally-consuming. In this work, we explore different machine learning (ML) methodologies to increase the resolution of molecular dynamics trajectories on-demand within a post-processing step. As a proof of concept, we analyse the performance of bi-directional neural networks such as neural ODEs, Hamiltonian networks, recurrent neural networks and LSTMs, as well as the uni-directional variants as a reference, for molecular dynamics simulations (here: the MD17 dataset). We have found that Bi-LSTMs are the best performing models; by utilizing the local time-symmetry of thermostated trajectories they can even learn long-range correlations and display high robustness to noisy dynamics across molecular complexity. Our models can reach accuracies of up to 10$^{-4}$ angstroms in trajectory interpolation, while faithfully reconstructing several full cycles of unseen intricate high-frequency molecular vibrations, rendering the comparison between the learned and reference trajectories indistinguishable. The results reported in this work can serve (1) as a baseline for larger systems, as well as (2) for the construction of better MD integrators.

Related papers

• 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)
• Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics [8.35780131268962]
  We present Implict Transfer Operator (ITO) Learning, a framework to learn surrogates of the simulation process with multiple time-resolutions. We also present a coarse-grained CG-SE3-ITO model which can quantitatively model all-atom molecular dynamics.
  arXiv  Detail & Related papers  (2023-05-29T12:19:41Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules [69.05950120497221]
  We propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets.
  arXiv  Detail & Related papers  (2022-10-29T07:12:46Z)
• 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)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic [38.50309739333058]
  We introduce an optimized implementation of the hybrid method (NNP/MM), which combines neural network potentials (NNP) and molecular mechanics (MM) This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. It has enabled us to increase the simulation speed by 5 times and achieve a combined sampling of one microsecond for each complex, marking the longest simulations ever reported for this class of simulation.
  arXiv  Detail & Related papers  (2022-01-20T10:57:20Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)
• Symmetry-adapted graph neural networks for constructing molecular dynamics force fields [10.820190246285122]
  We develop a symmetry-adapted graph neural networks framework to construct force fields automatically for molecular dynamics simulations. We show that MDGNN accurately reproduces the results of both classical and first-principles molecular dynamics.
  arXiv  Detail & Related papers  (2021-01-08T09:32:24Z)
• Molecular Latent Space Simulators [8.274472944075713]
  We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories. We demonstrate the approach in an application to Trp-protein to produce novel ultra-long synthetic folding trajectories.
  arXiv  Detail & Related papers  (2020-07-01T20:05:27Z)

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.