Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
Machine learning encompasses tools and algorithms that are now becoming popular in
almost all scientific and technological fields. This is true for molecular dynamics as well …
almost all scientific and technological fields. This is true for molecular dynamics as well …
Approaching complexity by stochastic methods: From biological systems to turbulence
This review addresses a central question in the field of complex systems: given a fluctuating
(in time or space), sequentially measured set of experimental data, how should one analyze …
(in time or space), sequentially measured set of experimental data, how should one analyze …
VAMPnets for deep learning of molecular kinetics
There is an increasing demand for computing the relevant structures, equilibria, and long-
timescale kinetics of biomolecular processes, such as protein-drug binding, from high …
timescale kinetics of biomolecular processes, such as protein-drug binding, from high …
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
C Wehmeyer, F Noé - The Journal of chemical physics, 2018 - pubs.aip.org
Inspired by the success of deep learning techniques in the physical and chemical sciences,
we apply a modification of an autoencoder type deep neural network to the task of …
we apply a modification of an autoencoder type deep neural network to the task of …
Non-Markovian modeling of protein folding
C Ayaz, L Tepper, FN Brünig… - Proceedings of the …, 2021 - National Acad Sciences
We extract the folding free energy landscape and the time-dependent friction function, the
two ingredients of the generalized Langevin equation (GLE), from explicit-water molecular …
two ingredients of the generalized Langevin equation (GLE), from explicit-water molecular …
Building insightful, memory-enriched models to capture long-time biochemical processes from short-time simulations
The ability to predict and understand complex molecular motions occurring over diverse
timescales ranging from picoseconds to seconds and even hours in biological systems …
timescales ranging from picoseconds to seconds and even hours in biological systems …
Construction of the free energy landscape of biomolecules via dihedral angle principal component analysis
A systematic approach to construct a low-dimensional free energy landscape from a
classical molecular dynamics (MD) simulation is presented. The approach is based on the …
classical molecular dynamics (MD) simulation is presented. The approach is based on the …
Data-driven non-Markovian closure models
This paper has two interrelated foci:(i) obtaining stable and efficient data-driven closure
models by using a multivariate time series of partial observations from a large-dimensional …
models by using a multivariate time series of partial observations from a large-dimensional …
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 …
Time-dependent friction effects on vibrational infrared frequencies and line shapes of liquid water
FN Brünig, O Geburtig, A Canal… - The Journal of …, 2022 - ACS Publications
From ab initio simulations of liquid water, the time-dependent friction functions and time-
averaged nonlinear effective bond potentials for the OH stretch and HOH bend vibrations …
averaged nonlinear effective bond potentials for the OH stretch and HOH bend vibrations …