Abstract
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.
Highlights
Colours have been attracting the attention of humans for a long time and are one key aspect that makes chemistry interesting.[1]
Colours can be in uenced by very subtle effects which led some authors to conclude that “the prediction of the colouring properties of yet unsynthesised compounds is a very risky business which still remains in the realm of art rather than of science”
We focus on the colours of metal–organic frameworks (MOFs)
Summary
Colours have been attracting the attention of humans for a long time and are one key aspect that makes chemistry interesting.[1]. One interesting case to understand how the model learns is the case of HKUST-1 for which Muller et al have shown that the green-blue colour that is typically observed for powders of this material is due to d–d transitions in defective paddlewheels (in perfect structures, the selection rules for the D4h symmetry lead to only weak transitions).[8] Not surprisingly, our model predicts a blue colour for this MOF as this is the colour reported in the CSD for HKUST-1 (CSD reference codes BODPAN, FIQCEN, but we excluded it from the training set). Since the model learns chemical similarities in some descriptor space it will predict similar colours for similar MOFs that might have similar defects—which might not be directly clear from the crystal structure, as in the case of HKUST-1
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