If machines can learn, who needs scientists?

Abstract

Machine learning has been used in NMR in for decades, but recent developments signal explosive growth is on the horizon. An obstacle to the application of machine learning in NMR is the relative paucity of available training data, despite the existence of numerous public NMR data repositories. Other challenges include the problem of interpreting the results of a machine learning algorithm, and incorporating machine learning into hypothesis-driven research. This perspective imagines the potential of machine learning in NMR and speculates on possible approaches to the hurdles.

Keywords: Databases; Machine learning; Spectrum analysis.