Conformational changes underpin function and encode complex biomolecular mechanisms.
Gaining atomic-level detail of how such changes occur has the potential to reveal these …

We derive a data-driven method for the approximation of the Koopman generator called
gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic …

With the rapid increase of available data for complex systems, there is great interest in the
extraction of physically relevant information from massive datasets. Recently, a framework …

One of the main challenges in molecular dynamics is overcoming the 'timescale barrier': in
many realistic molecular systems, biologically important rare transitions occur on timescales …

The aim of molecular coarse-graining approaches is to recover relevant physical properties
of the molecular system via a lower-resolution model that can be more efficiently simulated …

Generalized Langevin equations with non-linear forces and position-dependent linear
friction memory kernels, such as commonly used to describe the effective dynamics of …

Identifying optimal collective variables to model transformations using atomic-scale
simulations is a long-standing challenge. We propose a new method for the generation …

The generalized Langevin equation is widely used to model the effective dynamics of
chemical, soft or biological systems. It is used to describe the evolution of a small number of …

The study of phenomena such as protein folding and conformational changes in molecules
is a central theme in chemical physics. Molecular dynamics (MD) simulation is the primary …

In this work, we introduce a flow based machine learning approach called reaction
coordinate (RC) flow for the discovery of low-dimensional kinetic models of molecular …