Memory unlocks the future of biomolecular dynamics: Transformative tools to uncover physical insights accurately and efficiently
Conformational changes underpin function and encode complex biomolecular mechanisms.
Gaining atomic-level detail of how such changes occur has the potential to reveal these …
Gaining atomic-level detail of how such changes occur has the potential to reveal these …
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
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 …
gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic …
[HTML][HTML] Sparse learning of stochastic dynamical equations
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 …
extraction of physically relevant information from massive datasets. Recently, a framework …
Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning
C Schütte, S Klus, C Hartmann - Acta Numerica, 2023 - cambridge.org
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 …
many realistic molecular systems, biologically important rare transitions occur on timescales …
Slicing and dicing: Optimal coarse-grained representation to preserve molecular kinetics
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 …
of the molecular system via a lower-resolution model that can be more efficiently simulated …
Position-dependent memory kernel in generalized Langevin equations: Theory and numerical estimation
H Vroylandt, P Monmarché - The Journal of Chemical Physics, 2022 - pubs.aip.org
Generalized Langevin equations with non-linear forces and position-dependent linear
friction memory kernels, such as commonly used to describe the effective dynamics of …
friction memory kernels, such as commonly used to describe the effective dynamics of …
Data-driven path collective variables
Identifying optimal collective variables to model transformations using atomic-scale
simulations is a long-standing challenge. We propose a new method for the generation …
simulations is a long-standing challenge. We propose a new method for the generation …
On the derivation of the generalized Langevin equation and the fluctuation-dissipation theorem
H Vroylandt - Europhysics Letters, 2022 - iopscience.iop.org
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 …
chemical, soft or biological systems. It is used to describe the evolution of a small number of …
[HTML][HTML] Computing committors via Mahalanobis diffusion maps with enhanced sampling data
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 …
is a central theme in chemical physics. Molecular dynamics (MD) simulation is the primary …
Reaction coordinate flows for model reduction of molecular kinetics
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 …
coordinate (RC) flow for the discovery of low-dimensional kinetic models of molecular …