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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2023
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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. |
format | Online Article Text |
id | pubmed-9963467 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
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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