<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Giberti, Federico</dc:creator> <dc:creator>Cheng, Bingqing</dc:creator> <dc:creator>Tribello, Gareth</dc:creator> <dc:creator>Ceriotti, Michele</dc:creator> <dc:date>2020-04-27</dc:date> <dc:description>This repository contains the PLUMED-2 input files required to generate the data used in the ITRE publications. ITRE is a method to reweight Molecular Dynamics trajectory biased with a history-dependent potential (such as Metadynamics), and calculate unbiased thermodynamic observables. Since it uses the trajectory itself as a proxy of a grid, its scaling does not depend on the dimensionality of the space explored. In addition, thanks to its formulation, it is less affected by out-of-equilibrium points that characterize the early stages of a biased calculation and are usually discarded. Thanks to this feature, it can be used to increase the statistics when evaluating a thermodynamic observable from biased calculation.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2020.0044/v1</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:2020.0044/v1</dc:identifier> <dc:identifier>mcid:2020.0044/v1</dc:identifier> <dc:identifier>oai:materialscloud.org:377</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>Enhanced Sampling</dc:subject> <dc:subject>Molecular Dynamics</dc:subject> <dc:subject>Statistical Mechanics</dc:subject> <dc:subject>EPFL</dc:subject> <dc:subject>ERC</dc:subject> <dc:subject>SNSF</dc:subject> <dc:title>Iterative unbiasing of quasi-equilibrium sampling</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>