Federico Giberti^1*, Gareth Tribello^2*, Michele Ceriotti^1*

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), https://doi.org/10.24435/materialscloud:py-h3

Description

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

Materials Cloud sections using this data

Files

File name	Size	Description
atlas_inputs_plumed.zip MD5md5:b30be7a1a0673cc732fbf85dffd0f291	232.6 MiB	zip files containing the inputs for plumed-2.0 to reproduce ATLAS calculation

License

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

Keywords

enhanced sampling molecular dynamics machine learning SNSF EPFL ERC