Machine Learning of coarse-grained Molecular Dynamics Force Fields

no code implementations • 4 Dec 2018 • Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adria Perez, Nicholas E. Charron, Gianni de Fabritiis, Frank Noe, Cecilia Clementi

We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface.

BIG-bench Machine Learning Dimensionality Reduction +1

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

no code implementations • 30 Oct 2017 • Christoph Wehmeyer, Frank Noé

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 dimension reduction of molecular dynamics data.

Dimensionality Reduction