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Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights
Organic molecules tend to close pack to form dense structures when they are crystallised from organic solvents. Porous molecular crystals defy this rule: they contain open space, which is typically stabilised by inclusion of solvent in the interconnected pores during crystallisation. The design and...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Royal Society of Chemistry
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6991173/ https://www.ncbi.nlm.nih.gov/pubmed/32055355 http://dx.doi.org/10.1039/c9sc02832c |
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author | Cui, Peng McMahon, David P. Spackman, Peter R. Alston, Ben M. Little, Marc A. Day, Graeme M. Cooper, Andrew I. |
author_facet | Cui, Peng McMahon, David P. Spackman, Peter R. Alston, Ben M. Little, Marc A. Day, Graeme M. Cooper, Andrew I. |
author_sort | Cui, Peng |
collection | PubMed |
description | Organic molecules tend to close pack to form dense structures when they are crystallised from organic solvents. Porous molecular crystals defy this rule: they contain open space, which is typically stabilised by inclusion of solvent in the interconnected pores during crystallisation. The design and discovery of such structures is often challenging and time consuming, in part because it is difficult to predict solvent effects on crystal form stability. Here, we combine crystal structure prediction (CSP) with a robotic crystallisation screen to accelerate the discovery of stable hydrogen-bonded frameworks. We exemplify this strategy by finding new phases of two well-studied molecules in a computationally targeted way. Specifically, we find a new ‘hidden’ porous polymorph of trimesic acid, δ-TMA, that has a guest-free hexagonal pore structure, as well as three new solvent-stabilized diamondoid frameworks of adamantane-1,3,5,7-tetracarboxylic acid (ADTA). Beyond porous solids, this hybrid computational–experimental approach could be applied to a wide range of materials problems, such as organic electronics and drug formulation. |
format | Online Article Text |
id | pubmed-6991173 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-69911732020-02-13 Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights Cui, Peng McMahon, David P. Spackman, Peter R. Alston, Ben M. Little, Marc A. Day, Graeme M. Cooper, Andrew I. Chem Sci Chemistry Organic molecules tend to close pack to form dense structures when they are crystallised from organic solvents. Porous molecular crystals defy this rule: they contain open space, which is typically stabilised by inclusion of solvent in the interconnected pores during crystallisation. The design and discovery of such structures is often challenging and time consuming, in part because it is difficult to predict solvent effects on crystal form stability. Here, we combine crystal structure prediction (CSP) with a robotic crystallisation screen to accelerate the discovery of stable hydrogen-bonded frameworks. We exemplify this strategy by finding new phases of two well-studied molecules in a computationally targeted way. Specifically, we find a new ‘hidden’ porous polymorph of trimesic acid, δ-TMA, that has a guest-free hexagonal pore structure, as well as three new solvent-stabilized diamondoid frameworks of adamantane-1,3,5,7-tetracarboxylic acid (ADTA). Beyond porous solids, this hybrid computational–experimental approach could be applied to a wide range of materials problems, such as organic electronics and drug formulation. Royal Society of Chemistry 2019-09-17 /pmc/articles/PMC6991173/ /pubmed/32055355 http://dx.doi.org/10.1039/c9sc02832c Text en This journal is © The Royal Society of Chemistry 2019 http://creativecommons.org/licenses/by-nc/3.0/ This article is freely available. This article is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported Licence (CC BY-NC 3.0) |
spellingShingle | Chemistry Cui, Peng McMahon, David P. Spackman, Peter R. Alston, Ben M. Little, Marc A. Day, Graeme M. Cooper, Andrew I. Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights |
title | Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights
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title_full | Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights
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title_fullStr | Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights
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title_full_unstemmed | Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights
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title_short | Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights
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title_sort | mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6991173/ https://www.ncbi.nlm.nih.gov/pubmed/32055355 http://dx.doi.org/10.1039/c9sc02832c |
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