74 citations · 382 across the 17 of their papers we have counts for
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Machine Learning Implicit Solvation for Molecular Dynamics
Yaoyi Chen, Andreas Krämer, Nicholas E. Charron +3
Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scale…
Temperature-steerable flows
Manuel Dibak, Leon Klein, Frank Noé
Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples of a physical syste…
Deep learning Markov and Koopman models with physical constraints
Andreas Mardt, Luca Pasquali, Frank Noé +1
The long-timescale behavior of complex dynamical systems can be described by linear Markov or Koopman models in a suitable latent space. Recent variational approaches allow the lat…
Neural Mode Jump Monte Carlo
Luigi Sbailò, Manuel Dibak, Frank Noé
Markov chain Monte Carlo methods are a powerful tool for sampling equilibrium configurations in complex systems. One problem these methods often face is slow convergence over large…
xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states
Antonia S. J. S. Mey, Hao Wu, Frank Noé
Computing the equilibrium properties of complex systems, such as free energy differences, is often hampered by rare events in the dynamics. Enhanced sampling methods may be used in…