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Driving and characterizing nucleation of urea and glycine polymorphs in water

Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typi...

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Autores principales: Zou, Ziyue, Beyerle, Eric R., Tsai, Sun-Ting, Tiwary, Pratyush
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9963467/
https://www.ncbi.nlm.nih.gov/pubmed/36757888
http://dx.doi.org/10.1073/pnas.2216099120
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author Zou, Ziyue
Beyerle, Eric R.
Tsai, Sun-Ting
Tiwary, Pratyush
author_facet Zou, Ziyue
Beyerle, Eric R.
Tsai, Sun-Ting
Tiwary, Pratyush
author_sort Zou, Ziyue
collection PubMed
description Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here, we employ the machine learning-augmented molecular dynamics framework “reweighted autoencoded variational Bayes for enhanced sampling (RAVE).” We study two molecular systems—urea and glycine—in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth nucleation events of different polymorphs from homogeneous solution; from these trajectories, we calculate the relative ranking of finite-sized polymorph crystals embedded in solution, in terms of the free-energy difference between the finite-sized crystal polymorph and the original solution state. We further observe that the obtained reaction coordinates and transitions are highly nonclassical.
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spelling pubmed-99634672023-08-09 Driving and characterizing nucleation of urea and glycine polymorphs in water Zou, Ziyue Beyerle, Eric R. Tsai, Sun-Ting Tiwary, Pratyush Proc Natl Acad Sci U S A Physical Sciences Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here, we employ the machine learning-augmented molecular dynamics framework “reweighted autoencoded variational Bayes for enhanced sampling (RAVE).” We study two molecular systems—urea and glycine—in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth nucleation events of different polymorphs from homogeneous solution; from these trajectories, we calculate the relative ranking of finite-sized polymorph crystals embedded in solution, in terms of the free-energy difference between the finite-sized crystal polymorph and the original solution state. We further observe that the obtained reaction coordinates and transitions are highly nonclassical. National Academy of Sciences 2023-02-09 2023-02-14 /pmc/articles/PMC9963467/ /pubmed/36757888 http://dx.doi.org/10.1073/pnas.2216099120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Zou, Ziyue
Beyerle, Eric R.
Tsai, Sun-Ting
Tiwary, Pratyush
Driving and characterizing nucleation of urea and glycine polymorphs in water
title Driving and characterizing nucleation of urea and glycine polymorphs in water
title_full Driving and characterizing nucleation of urea and glycine polymorphs in water
title_fullStr Driving and characterizing nucleation of urea and glycine polymorphs in water
title_full_unstemmed Driving and characterizing nucleation of urea and glycine polymorphs in water
title_short Driving and characterizing nucleation of urea and glycine polymorphs in water
title_sort driving and characterizing nucleation of urea and glycine polymorphs in water
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9963467/
https://www.ncbi.nlm.nih.gov/pubmed/36757888
http://dx.doi.org/10.1073/pnas.2216099120
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