20 citations · 26 across the 3 of their papers we have counts for
6 papers
Auto-Encoding Molecular Conformations
Robin Winter, Frank Noé, Djork-Arné Clevert
In this work we introduce an Autoencoder for molecular conformations. Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformatio…
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…
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties
Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt +1
Equivariant neural networks (ENNs) are graph neural networks embedded in and are well suited for predicting molecular properties. The ENN library e3nn has customizab…
Stochastic Normalizing Flows
Hao Wu, Jonas Köhler, Frank Noé
The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics. While classical stoch…
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…
Deflation reveals dynamical structure in nondominant reaction coordinates
Brooke E. Husic, Frank Noé
The output of molecular dynamics simulations is high-dimensional, and the degrees of freedom among the atoms are related in intricate ways. Therefore, a variety of analysis framewo…