Coarse-graining Molecular Systems by Spectral Matching

Abstract

Coarse-graining has become an area of tremendous importance within many different research fields. For molecular simulation, coarse-graining bears the promise of finding simplified models such that long-time simulations of large-scale systems become computationally tractable. While significant progress has been made in tuning thermodynamic properties of reduced models, it remains a key challenge to ensure that relevant kinetic properties are retained by coarse-grained dynamical systems. In this study, we focus on data-driven methods to preserve the rare-event kinetics of the original system, and make use of their close connection to the low-lying spectrum of the system's generator. Building on work by Crommelin and Vanden-Eijnden, SIAM Multiscale Model. Simul. (2011), we present a general framework, called spectral matching, which directly targets the generator's leading eigenvalue equations when learning parameters for coarse-grained models. We discuss different parametric models for effective dynamics and derive the resulting data-based regression problems. We show that spectral matching can be used to learn effective potentials which retain the slow dynamics, but also to correct the dynamics induced by existing techniques, such as force matching.

References in corpus (6)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• VAMPnets: Deep learning of molecular kinetics
• Sparse learning of stochastic dynamic equations
• DeePCG: constructing coarse-grained models via deep neural networks
• Effective dynamics using conditional expectations
• Markov State Models from short non-Equilibrium Simulations - Analysis and Correction of Estimation Bias

Cited by in corpus (12)

• Machine Learning Force Fields
• Machine learning for molecular simulation
• Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
• TorchMD: A deep learning framework for molecular simulations
• Coarse Graining Molecular Dynamics with Graph Neural Networks
• Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
• Machine Learning Implicit Solvation for Molecular Dynamics
• Flow-matching -- efficient coarse-graining of molecular dynamics without forces
• Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
• Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
• Free Energy Landscapes, Diffusion Coefficients, and Kinetic Rates from Transition Paths
• Consistent Projection of Langevin Dynamics: Preserving Thermodynamics and Kinetics in Coarse-Grained Models