Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach

• URL: http://arxiv.org/abs/2005.01851v1
• Date: Mon, 4 May 2020 21:20:01 GMT
• Title: Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
• Authors: Jiang Wang, Stefan Chmiela, Klaus-Robert M\"uller, Frank No\`e, Cecilia Clementi
• Abstract summary: Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field. We demonstrate its application to learn an effective coarse-grained (CG) model from all-atom simulation data. Using ensemble learning and stratified sampling, we propose a data-efficient and memory-saving alternative.
• Score: 2.562811344441631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field based on the kernel ridge regression algorithm. Here, we demonstrate its application to learn an effective coarse-grained (CG) model from all-atom simulation data in a sample efficient manner. The coarse-grained force field is learned by following the thermodynamic consistency principle, here by minimizing the error between the predicted coarse-grained force and the all-atom mean force in the coarse-grained coordinates. Solving this problem by GDML directly is impossible because coarse-graining requires averaging over many training data points, resulting in impractical memory requirements for storing the kernel matrices. In this work, we propose a data-efficient and memory-saving alternative. Using ensemble learning and stratified sampling, we propose a 2-layer training scheme that enables GDML to learn an effective coarse-grained model. We illustrate our method on a simple biomolecular system, alanine dipeptide, by reconstructing the free energy landscape of a coarse-grained variant of this molecule. Our novel GDML training scheme yields a smaller free energy error than neural networks when the training set is small, and a comparably high accuracy when the training set is sufficiently large.

Related papers

• chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics [0.0]
  Neural Networks (NNs) are promising models for refining the accuracy of molecular dynamics. Chemtrain is a framework to learn sophisticated NN potential models through customizable training routines and advanced training algorithms.
  arXiv  Detail & Related papers  (2024-08-28T15:14:58Z)
• 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)
• Gradual Optimization Learning for Conformational Energy Minimization [69.36925478047682]
  Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks significantly reduces the required additional data. Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules.
  arXiv  Detail & Related papers  (2023-11-05T11:48:08Z)
• Transfer learning for atomistic simulations using GNNs and kernel mean embeddings [24.560340485988128]
  We propose a transfer learning algorithm that leverages the ability of graph neural networks (GNNs) to represent chemical environments together with kernel mean embeddings. We test our approach on a series of realistic datasets of increasing complexity, showing excellent generalization and transferability performance.
  arXiv  Detail & Related papers  (2023-06-02T14:58:16Z)
• Learning Controllable Adaptive Simulation for Multi-resolution Physics [86.8993558124143]
  We introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model. LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening. We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error.
  arXiv  Detail & Related papers  (2023-05-01T23:20:27Z)
• Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics [55.41644538483948]
  coarse-grained (CG) models have the potential to simulate large molecular complexes beyond what is possible with atomistic molecular dynamics. A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average. We show that there is flexibility in how to map all-atom forces to the CG representation, and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation.
  arXiv  Detail & Related papers  (2023-02-14T14:35:39Z)
• Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation [59.45669299295436]
  We propose a Monte Carlo PDE solver for training unsupervised neural solvers. We use the PDEs' probabilistic representation, which regards macroscopic phenomena as ensembles of random particles. Our experiments on convection-diffusion, Allen-Cahn, and Navier-Stokes equations demonstrate significant improvements in accuracy and efficiency.
  arXiv  Detail & Related papers  (2023-02-10T08:05:19Z)
• A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification [21.972192114861873]
  We propose a hybrid data driven-physics constrained Gaussian process regression framework. We encode the physics knowledge with Boltzmann-Gibbs distribution and derive our model through maximum likelihood (ML) approach. The proposed model achieves good results in high-dimensional problem, and correctly propagate the uncertainty, with very limited labelled data provided.
  arXiv  Detail & Related papers  (2022-05-13T07:53:49Z)
• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles [60.12186997181117]
  Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements. We propose GeoMol, an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate 3D conformers.
  arXiv  Detail & Related papers  (2021-06-08T14:17:59Z)
• A hybrid MGA-MSGD ANN training approach for approximate solution of linear elliptic PDEs [0.0]
  We introduce a hybrid "Modified Genetic-Multilevel Gradient Descent" (MGA-MSGD) training algorithm. It considerably improves accuracy and efficiency of solving 3D mechanical problems described, in strong-form, by PDEs via ANNs.
  arXiv  Detail & Related papers  (2020-12-18T10:59:07Z)
• Particles to Partial Differential Equations Parsimoniously [0.0]
  coarse-grained effective Partial Differential Equations can lead to considerable savings in computation-intensive tasks like prediction or control. We propose a framework combining artificial neural networks with multiscale computation, in the form of equation-free numerics. We illustrate our approach by extracting coarse-grained evolution equations from particle-based simulations with a priori unknown macro-scale variable.
  arXiv  Detail & Related papers  (2020-11-09T15:51:24Z)

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.