Learning data efficient coarse-grained molecular dynamics from forces and noise

Zenodo (CERN European Organization for Nuclear Research) 2026 Open access

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Authors: Y Chen, Aleksander E. P. Durumeric, Aldo Pasos-Trejo, Frank Noé, Cecilia Clementi
DOI: 10.5281/zenodo.18756638
Cited by: 1

Abstract

Demo and code examples for the "Learning data efficient coarse-grained molecular dynamics from1 forces and noise" paper Incldues an Ubiquitin dataset with steered molecular dynamics and folded basin simulations. Please see the README.md for detailed instructions on how to use the contents.

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
24 February 2026