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Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores

Using molecular simulations, we study the processes of capillary condensation and capillary evaporation in model mesopores. To determine the phase transition pathway, as well as the corresponding free energy profile, we carry out enhanced sampling molecular simulations using entropy as a reaction co...

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Autores principales: Desgranges, Caroline, Delhommelle, Jerome
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774451/
https://www.ncbi.nlm.nih.gov/pubmed/35052123
http://dx.doi.org/10.3390/e24010097
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author Desgranges, Caroline
Delhommelle, Jerome
author_facet Desgranges, Caroline
Delhommelle, Jerome
author_sort Desgranges, Caroline
collection PubMed
description Using molecular simulations, we study the processes of capillary condensation and capillary evaporation in model mesopores. To determine the phase transition pathway, as well as the corresponding free energy profile, we carry out enhanced sampling molecular simulations using entropy as a reaction coordinate to map the onset of order during the condensation process and of disorder during the evaporation process. The structural analysis shows the role played by intermediate states, characterized by the onset of capillary liquid bridges and bubbles. We also analyze the dependence of the free energy barrier on the pore width. Furthermore, we propose a method to build a machine learning model for the prediction of the free energy surfaces underlying capillary phase transition processes in mesopores.
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spelling pubmed-87744512022-01-21 Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores Desgranges, Caroline Delhommelle, Jerome Entropy (Basel) Article Using molecular simulations, we study the processes of capillary condensation and capillary evaporation in model mesopores. To determine the phase transition pathway, as well as the corresponding free energy profile, we carry out enhanced sampling molecular simulations using entropy as a reaction coordinate to map the onset of order during the condensation process and of disorder during the evaporation process. The structural analysis shows the role played by intermediate states, characterized by the onset of capillary liquid bridges and bubbles. We also analyze the dependence of the free energy barrier on the pore width. Furthermore, we propose a method to build a machine learning model for the prediction of the free energy surfaces underlying capillary phase transition processes in mesopores. MDPI 2022-01-07 /pmc/articles/PMC8774451/ /pubmed/35052123 http://dx.doi.org/10.3390/e24010097 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Desgranges, Caroline
Delhommelle, Jerome
Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores
title Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores
title_full Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores
title_fullStr Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores
title_full_unstemmed Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores
title_short Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores
title_sort machine-learned free energy surfaces for capillary condensation and evaporation in mesopores
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774451/
https://www.ncbi.nlm.nih.gov/pubmed/35052123
http://dx.doi.org/10.3390/e24010097
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