Machine learning for chemical discovery
Journal: Nature Communications
•
• Nature Portfolio
• ISSN:
2041-1723
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.
Subjects & Keywords
How to Cite this Article
Alexandre Tkatchenko (2020). "Machine learning for chemical discovery". Nature Communications. https://doi.org/10.1038/s41467-020-17844-8