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. 2021 Jun 7;8:676976. doi: 10.3389/fmolb.2021.676976

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Copyright © 2021 Giulini, Rigoli, Mattiotti, Menichetti, Tarenzi, Fiorentini and Potestio.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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(A) The mapping entropy is defined as the Kullback-Leibler distance between the atomistic, fully detailed probability distribution (left panel) and its blurred reconstruction (right panel), obtained from a reduced description obtained neglecting a subset of degrees of freedom (middle panel). (B) The algorithmic implementation of the method introduced by Giulini and coworkers (Giulini et al., 2020) iteratively refines the CG mapping until convergence: the resulting, optimal SLR generates a reconstructed FLR that adheres as much as possible to the original one.