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A Data-Driven Perspective on the Colours of Metal-Organic Frameworks

submitted on 01.10.2020, 11:20 and posted on 01.10.2020, 13:44 by Kevin Maik Jablonka, Seyed Mohamad Moosavi, Mehrdad Asgari, Christopher Ireland, Luc Patiny, Berend Smit

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—and chemical data in general—that is more suitable for a data-driven approach to chemistry. All data is captured in an electronic lab notebook and subsequently exported to a repository.


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Declaration of Conflict of Interest

No conflict of interest.