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4 papers
A Unified Framework for Quantifying Privacy Risk in Synthetic Data
Matteo Giomi, Franziska Boenisch, Christoph Wehmeyer +1
Synthetic data is often presented as a method for sharing sensitive information in a privacy-preserving manner by reproducing the global statistical properties of the original data…
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…
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
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 dimensi…
Markov State Models from short non-Equilibrium Simulations - Analysis and Correction of Estimation Bias
Feliks Nüske, Hao Wu, Jan-Hendrik Prinz +3
Many state of the art methods for the thermodynamic and kinetic characterization of large and complex biomolecular systems by simulation rely on ensemble approaches, where data fro…