Self-learning Multiscale Simulation for Achieving High Accuracy and High Efficiency Simultaneously

TL;DR

This paper introduces a self-learning multiscale molecular dynamics simulation method that achieves high accuracy and efficiency by iteratively improving coarse-grained potentials without prior knowledge, demonstrated on biomolecular systems.

Contribution

A novel self-learning strategy for multiscale simulations that enhances sampling efficiency and accuracy without needing pre-defined coarse-grained potentials.

Findings

Rapid improvement of CG potential from unrealistic starting points

Achieved free energy results consistent with exact calculations

Faster convergence compared to traditional replica exchange methods

Abstract

We propose a new multi-scale molecular dynamics simulation method which can achieve high accuracy and high sampling efficiency simultaneously without aforehand knowledge of the coarse grained (CG) potential and test it for a biomolecular system. Based on the resolution exchange simulations between atomistic and CG replicas, a self-learning strategy is introduced to progressively improve the CG potential by an iterative way. Two tests show that, the new method can rapidly improve the CG potential and achieve efficient sampling even starting from an unrealistic CG potential. The resulting free energy agreed well with exact result and the convergence by the method was much faster than that by the replica exchange method. The method is generic and can be applied to many biological as well as non-biological problems.