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A data-driven perspective on the colours of metal–organic frameworks

Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In...

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Autores principales: Jablonka, Kevin Maik, Moosavi, Seyed Mohamad, Asgari, Mehrdad, Ireland, Christopher, Patiny, Luc, Smit, Berend
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179528/
https://www.ncbi.nlm.nih.gov/pubmed/34163632
http://dx.doi.org/10.1039/d0sc05337f
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author Jablonka, Kevin Maik
Moosavi, Seyed Mohamad
Asgari, Mehrdad
Ireland, Christopher
Patiny, Luc
Smit, Berend
author_facet Jablonka, Kevin Maik
Moosavi, Seyed Mohamad
Asgari, Mehrdad
Ireland, Christopher
Patiny, Luc
Smit, Berend
author_sort Jablonka, Kevin Maik
collection PubMed
description Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general. All data is captured in an objective, and standardised, form in an electronic lab notebook and subsequently automatically exported to a repository in open formats, from where it can be interactively explored by other researchers. We envision this to be key for a data-driven approach to chemical research.
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spelling pubmed-81795282021-06-22 A data-driven perspective on the colours of metal–organic frameworks Jablonka, Kevin Maik Moosavi, Seyed Mohamad Asgari, Mehrdad Ireland, Christopher Patiny, Luc Smit, Berend Chem Sci Chemistry Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general. All data is captured in an objective, and standardised, form in an electronic lab notebook and subsequently automatically exported to a repository in open formats, from where it can be interactively explored by other researchers. We envision this to be key for a data-driven approach to chemical research. The Royal Society of Chemistry 2020-12-28 /pmc/articles/PMC8179528/ /pubmed/34163632 http://dx.doi.org/10.1039/d0sc05337f Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/
spellingShingle Chemistry
Jablonka, Kevin Maik
Moosavi, Seyed Mohamad
Asgari, Mehrdad
Ireland, Christopher
Patiny, Luc
Smit, Berend
A data-driven perspective on the colours of metal–organic frameworks
title A data-driven perspective on the colours of metal–organic frameworks
title_full A data-driven perspective on the colours of metal–organic frameworks
title_fullStr A data-driven perspective on the colours of metal–organic frameworks
title_full_unstemmed A data-driven perspective on the colours of metal–organic frameworks
title_short A data-driven perspective on the colours of metal–organic frameworks
title_sort data-driven perspective on the colours of metal–organic frameworks
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179528/
https://www.ncbi.nlm.nih.gov/pubmed/34163632
http://dx.doi.org/10.1039/d0sc05337f
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