Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics.

ABSTRACT: The potential of molecular simulations is limited by their computational costs. There is often a need to accelerate simulations using some of the enhanced sampling methods. Metadynamics applies a history-dependent bias potential that disfavors previously visited states. To apply metadynamics, it is necessary to select a few properties of the system─collective variables (CVs) that can be used to define the bias potential. Over the past few years, there have been emerging opportunities for machine learning and, in particular, artificial neural networks within this domain. In this broad context, a specific unsupervised machine learning method was utilized, namely, parametric time-lagged t-distributed stochastic neighbor embedding (ptltSNE) to design CVs. The approach was tested on a Trp-cage

SUBMITTER: Hradiska H 

PROVIDER: S-EPMC10839826 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature