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. 2019 Nov 26;7:809. doi: 10.3389/fchem.2019.00809

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Copyright © 2019 Cova and Pais.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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A holistic view of ML-based contributions in Chemistry. The clustering heatmap displays the relative counts of ML outcomes, within each area of Chemistry (organic, inorganic, analytical, physical, and biochemistry), in the 2008–2019 (30 June) period. Data are expressed as fractions of the highest number of publications, including articles, reviews and books, containing specific co-occurring keywords, and following a standard normalization procedure. Hierarchical clustering with Euclidean distances and Ward linkage was performed on both Chemistry sub-fields and type of application. Co-occurrences are colored using a yellow-to-red color scheme. Highest and lowest relative contributions correspond to 1 (red) and 0 (yellow) values, respectively.