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Machine Learning for Molecular Dynamics on Long Timescales
Frank Noé
Molecular Dynamics (MD) simulation is widely used to analyze the properties of molecules and materials. Most practical applications, such as comparison with experimental measuremen…
Machine Learning of coarse-grained Molecular Dynamics Force Fields
Jiang Wang, Simon Olsson, Christoph Wehmeyer +5
Atomistic or ab-initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond…
Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning
Frank Noé, Simon Olsson, Jonas Köhler +1
Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independen…
Identification of kinetic order parameters for non-equilibrium dynamics
Fabian Paul, Hao Wu, Maximilian Vossel +2
A popular approach to analyze the dynamics of high-dimensional many-body systems, such as macromolecules, is to project the trajectories onto a space of slowly-varying collective v…
The mechanism of RNA base fraying: molecular dynamics simulations analyzed with core-set Markov state models
Giovanni Pinamonti, Fabian Paul, Frank Noé +2
The process of RNA base fraying (i.e. the transient opening of the termini of a helix) is involved in many aspects of RNA dynamics. We here use molecular dynamics simulations and M…
Variational Selection of Features for Molecular Kinetics
Martin K. Scherer, Brooke E. Husic, Moritz Hoffmann +3
The modeling of atomistic biomolecular simulations using kinetic models such as Markov state models (MSMs) has had many notable algorithmic advances in recent years. The variationa…
Reversible Interacting-Particle Reaction Dynamics
Christoph Fröhner, Frank Noé
Interacting-Particle Reaction Dynamics (iPRD) simulates the spatiotemporal evolution of particles that experience interaction forces and can react with one another. The combination…
Deep Generative Markov State Models
Hao Wu, Andreas Mardt, Luca Pasquali +1
We propose a deep generative Markov State Model (DeepGenMSM) learning framework for inference of metastable dynamical systems and prediction of trajectories. After unsupervised tra…
Grand canonical diffusion-influenced reactions: a stochastic theory with applications to multiscale reaction-diffusion simulations
Mauricio J. del Razo, Hong Qian, Frank Noé
Smoluchowski-type models for diffusion-influenced reactions (A+B -> C) can be formulated within two frameworks: the probabilistic-based approach for a pair A, B of reacting particl…
Optimal data-driven estimation of generalized Markov state models for non-equilibrium dynamics
Péter Koltai, Hao Wu, Frank Noé +1
There are multiple ways in which a stochastic system can be out of statistical equilibrium. It might be subject to time-varying forcing; or be in a transient phase on its way towar…