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Technical University of Munich
Technical University of Munich

Software

Recent Papers:

JaxSGMC: Modular stochastic gradient MCMC in JAX

S. Thaler, P. Fuchs, A. Cukarska, J. Zavadlav, SoftwareX 2024, paper 

An application-agnostic library for stochastic gradient Markov chain Monte Carlo (SG-MCMC) in JAX, enabling Bayesian deep learning.

Published code:

Relative Entropy Minimization

GitHub 

Code for training of coarse-grained molecular models with relative entropy minimization.

Differentiable Trajectory Reweighting

GitHub Zenodo

Code for gradient-based training of molecular dynamics potentials from experimental data.

Jax / Haiku implementation of DimeNet++

GitHub

This DimeNet++ implementation can be used for molecular dynamics simulations with Jax, M.D. and for molecular property prediction.

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