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 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, using chemically meaningful reasoning. 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 that is more suitable for a data-driven approach to materials science.

DOI https://doi.org/10.24435/materialscloud:cc-j6
Source https://archive.materialscloud.org/record/2020.163
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:530
Creator Jablonka, Kevin Maik; Moosavi, Seyed Mohamad; Asgari, Mehrdad; Ireland, Christopher; Patiny, Luc; Smit, Berend
Publisher Materials Cloud
Publication Year 2020
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Language English
Resource Type Dataset
Discipline Materials Science and Engineering