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Diverse Phases of Carbonaceous Materials from Stochastic Simulations

[Image: see text] Amorphous carbon systems are emerging to have unparalleled properties at multiple length scales, making them the preferred choice for creating advanced materials in many sectors, but the lack of long-range order makes it difficult to establish structure/property relationships. We p...

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Autores principales: Monti, Susanna, Barcaro, Giovanni, Goddard, William A., Fortunelli, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9639862/
https://www.ncbi.nlm.nih.gov/pubmed/33721495
http://dx.doi.org/10.1021/acsnano.0c08029
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author Monti, Susanna
Barcaro, Giovanni
Goddard, William A.
Fortunelli, Alessandro
author_facet Monti, Susanna
Barcaro, Giovanni
Goddard, William A.
Fortunelli, Alessandro
author_sort Monti, Susanna
collection PubMed
description [Image: see text] Amorphous carbon systems are emerging to have unparalleled properties at multiple length scales, making them the preferred choice for creating advanced materials in many sectors, but the lack of long-range order makes it difficult to establish structure/property relationships. We propose an original computational approach to predict the morphology of carbonaceous materials for arbitrary densities that we apply here to graphitic phases at low densities from 1.15 to 0.16 g/cm(3), including glassy carbon. This approach, dynamic reactive massaging of the potential energy surface (DynReaxMas), uses the ReaxFF reactive force field in a simulation protocol that combines potential energy surface (PES) transformations with global optimization within a multidescriptor representation. DynReaxMas enables the simulation of materials synthesis at temperatures close to experiment to correctly capture the interplay of activated vs entropic processes and the resulting phase morphology. We then show that DynReaxMas efficiently and semiautomatically produces atomistic configurations that span wide relevant regions of the PES at modest computational costs. Indeed, we find a variety of distinct phases at the same density, and we illustrate the evolution of competing phases as a function of density ranging from uniform vs bimodal distributions of pore sizes at higher and intermediate density (1.15 g/cm(3) and 0.50 g/cm(3)) to agglomerated vs sparse morphologies, further partitioned into boxed vs hollow fibrillar morphologies, at lower density (0.16 g/cm(3)). Our observations of diverse phases at the same density agree with experiment. Some of our identified phases provide descriptors consistent with available experimental data on local density, pore sizes, and HRTEM images, showing that DynReaxMas provides a systematic classification of the complex field of amorphous carbonaceous materials that can provide 3D structures to interpret experimental observations.
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spelling pubmed-96398622022-11-08 Diverse Phases of Carbonaceous Materials from Stochastic Simulations Monti, Susanna Barcaro, Giovanni Goddard, William A. Fortunelli, Alessandro ACS Nano [Image: see text] Amorphous carbon systems are emerging to have unparalleled properties at multiple length scales, making them the preferred choice for creating advanced materials in many sectors, but the lack of long-range order makes it difficult to establish structure/property relationships. We propose an original computational approach to predict the morphology of carbonaceous materials for arbitrary densities that we apply here to graphitic phases at low densities from 1.15 to 0.16 g/cm(3), including glassy carbon. This approach, dynamic reactive massaging of the potential energy surface (DynReaxMas), uses the ReaxFF reactive force field in a simulation protocol that combines potential energy surface (PES) transformations with global optimization within a multidescriptor representation. DynReaxMas enables the simulation of materials synthesis at temperatures close to experiment to correctly capture the interplay of activated vs entropic processes and the resulting phase morphology. We then show that DynReaxMas efficiently and semiautomatically produces atomistic configurations that span wide relevant regions of the PES at modest computational costs. Indeed, we find a variety of distinct phases at the same density, and we illustrate the evolution of competing phases as a function of density ranging from uniform vs bimodal distributions of pore sizes at higher and intermediate density (1.15 g/cm(3) and 0.50 g/cm(3)) to agglomerated vs sparse morphologies, further partitioned into boxed vs hollow fibrillar morphologies, at lower density (0.16 g/cm(3)). Our observations of diverse phases at the same density agree with experiment. Some of our identified phases provide descriptors consistent with available experimental data on local density, pore sizes, and HRTEM images, showing that DynReaxMas provides a systematic classification of the complex field of amorphous carbonaceous materials that can provide 3D structures to interpret experimental observations. American Chemical Society 2021-03-15 2021-04-27 /pmc/articles/PMC9639862/ /pubmed/33721495 http://dx.doi.org/10.1021/acsnano.0c08029 Text en © 2021 American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Monti, Susanna
Barcaro, Giovanni
Goddard, William A.
Fortunelli, Alessandro
Diverse Phases of Carbonaceous Materials from Stochastic Simulations
title Diverse Phases of Carbonaceous Materials from Stochastic Simulations
title_full Diverse Phases of Carbonaceous Materials from Stochastic Simulations
title_fullStr Diverse Phases of Carbonaceous Materials from Stochastic Simulations
title_full_unstemmed Diverse Phases of Carbonaceous Materials from Stochastic Simulations
title_short Diverse Phases of Carbonaceous Materials from Stochastic Simulations
title_sort diverse phases of carbonaceous materials from stochastic simulations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9639862/
https://www.ncbi.nlm.nih.gov/pubmed/33721495
http://dx.doi.org/10.1021/acsnano.0c08029
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