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Global free-energy landscapes as a smoothly joined collection of local maps

Federico Giberti1*, Gareth Tribello2*, Michele Ceriotti1*

1 Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland

2 Atomistic Simulation Centre, School of Mathematics and Physics, Queen's University Belfast, Belfast, BT14 7EN, United Kingdom

* Corresponding authors emails: federico.giberti@epfl.ch, g.tribello@qub.ac.uk, michele.ceriotti@epfl.ch
DOI10.24435/materialscloud:py-h3 [version v1]

Publication date: May 08, 2021

How to cite this record

Federico Giberti, Gareth Tribello, Michele Ceriotti, Global free-energy landscapes as a smoothly joined collection of local maps, Materials Cloud Archive 2021.72 (2021), doi: 10.24435/materialscloud:py-h3.


This repository contains the scripts that were used to run the calculations that present a new biasing technique, the Adaptive Topography of Landscape for Accelerated Sampling (ATLAS). The techinque is implemented in plumed-2.0 and the input file are included in the repository, as well as a few scripts to postprocess the calculations and reproduce the plots presented in the paper

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File name Size Description
232.6 MiB zip files containing the inputs for plumed-2.0 to reproduce ATLAS calculation


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Journal reference (Paper in which the method is described and benchmarked)
Preprint (ArXiv preprint of the paper)


enhanced sampling molecular dynamics machine learning SNSF EPFL ERC

Version history:

2021.72 (version v1) [This version] May 08, 2021 DOI10.24435/materialscloud:py-h3