Machine learning for chemical discovery

Alexandre Tkatchenko ()
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Alexandre Tkatchenko: University of Luxembourg

Nature Communications, 2020, vol. 11, issue 1, 1-4

Abstract: Discovering chemicals with desired attributes is a long and painstaking process. Curated datasets containing reliable quantum-mechanical properties for millions of molecules are becoming increasingly available. The development of novel machine learning tools to obtain chemical knowledge from these datasets has the potential to revolutionize the process of chemical discovery. Here, I comment on recent breakthroughs in this emerging field and discuss the challenges for the years to come.

Date: 2020
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DOI: 10.1038/s41467-020-17844-8

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