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

Kevin Maik Jablonka1*, Seyed Mohamad Moosavi1, Mehrdad Asgari2,3, Christopher Ireland1, Luc Patiny4, Berend Smit1*

1 Laboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland

2 Institute of Mechanical Engineering (IGM), School of Engineering (STI), École Polytechnique Fédérale de Lausanne (EPFL), CH‐1015 Lausanne, Switzerland

3 Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland

4 Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH‐1015 Lausanne, Vaud, Switzerland

* Corresponding authors emails: kevin.jablonka@epfl.ch, berend.smit@epfl.ch
DOI10.24435/materialscloud:cc-j6 [version v1]

Publication date: Dec 22, 2020

How to cite this record

Kevin Maik Jablonka, Seyed Mohamad Moosavi, Mehrdad Asgari, Christopher Ireland, Luc Patiny, Berend Smit, A data-driven perspective on the colours of metal-organic frameworks, Materials Cloud Archive 2020.163 (2020), doi: 10.24435/materialscloud:cc-j6.

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 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.

Materials Cloud sections using this data

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Files

File name Size Description
README.txt
MD5md5:7d1f961bfefc2e8bc567eadf30a48eec
2.0 KiB README with detailed description of the contents of the zip
Archive.zip
MD5md5:b124f411ed37add72e4881f4fd13724a
882.7 MiB Archive containing the dataset, the models and the feature importance data

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Journal reference
K. M. Jablonka, S. M. Moosavi, M. Asgari, C. Ireland, L. Patiny, B. Smit, submitted.
Preprint

Keywords

LSMO MARVEL EPFL ERC ML Color

Version history:

2020.163 (version v1) [This version] Dec 22, 2020 DOI10.24435/materialscloud:cc-j6