Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls

• URL: http://arxiv.org/abs/2209.12948v1
• Date: Mon, 26 Sep 2022 18:32:37 GMT
• Title: Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls
• Authors: Eleonora Ricci, George Giannakopoulos, Vangelis Karkaletsis, Doros N. Theodorou, Niki Vergadou
• Abstract summary: Coarse graining (CG) enables the investigation of molecular properties for larger systems and at longer timescales than the ones attainable at the atomistic resolution. Machine learning techniques have been recently proposed to learn CG particle interactions, i.e. develop CG force fields. In this work, the force acting on each CG particle is correlated to a learned representation of its local environment that goes under the name of SchNet, constructed via continuous filter convolutions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coarse graining (CG) enables the investigation of molecular properties for larger systems and at longer timescales than the ones attainable at the atomistic resolution. Machine learning techniques have been recently proposed to learn CG particle interactions, i.e. develop CG force fields. Graph representations of molecules and supervised training of a graph convolutional neural network architecture are used to learn the potential of mean force through a force matching scheme. In this work, the force acting on each CG particle is correlated to a learned representation of its local environment that goes under the name of SchNet, constructed via continuous filter convolutions. We explore the application of SchNet models to obtain a CG potential for liquid benzene, investigating the effect of model architecture and hyperparameters on the thermodynamic, dynamical, and structural properties of the simulated CG systems, reporting and discussing challenges encountered and future directions envisioned.

Related papers

• Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks [36.136619420474766]
  We use a graph-convolutional neural network architecture to develop a highly automated training pipeline for coarse grained force fields. We show that this approach yields highly accurate force fields, but also that these force fields are more transferable through a variety of thermodynamic conditions.
  arXiv  Detail & Related papers  (2024-06-17T21:44:05Z)
• Invertible Coarse Graining with Physics-Informed Generative Artificial Intelligence [9.343446996260328]
  Two challenges are commonly present in multiscale molecular modeling. One is to construct coarse grained models by passing information from the fine to coarse levels; the other is to restore finer molecular details given coarse grained configurations. We present a theory connecting them, and develop a methodology called Cycle Coarse Graining (CCG) to solve both problems in a unified manner.
  arXiv  Detail & Related papers  (2023-05-02T08:05:42Z)
• 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)
• Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics [15.660348943139711]
  We leverage connections between score-based generative models, force fields and molecular dynamics to learn a CG force field without requiring any force inputs during training. While having a vastly simplified training setup compared to previous work, we demonstrate that our approach leads to improved performance across several small- to medium-sized protein simulations.
  arXiv  Detail & Related papers  (2023-02-01T17:09:46Z)
• 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)
• Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks [80.8446673089281]
  We study a new paradigm of knowledge transfer that aims at encoding graph topological information into graph neural networks (GNNs) We propose Neural Heat Kernel (NHK) to encapsulate the geometric property of the underlying manifold concerning the architecture of GNNs. A fundamental and principled solution is derived by aligning NHKs on teacher and student models, dubbed as Geometric Knowledge Distillation.
  arXiv  Detail & Related papers  (2022-10-24T08:01:58Z)
• 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)
• 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)
• Coarse Graining Molecular Dynamics with Graph Neural Networks [3.0279361008741827]
  We introduce a hybrid architecture for the machine learning of coarse-grained force fields that learns their own features via a subnetwork. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems.
  arXiv  Detail & Related papers  (2020-07-22T13:20:08Z)
• 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.