  Theoretical predictions of NMR chemical shifts from first-principles can
greatly facilitate experimental interpretation and structure identification.
However, accurate prediction of chemical shifts using the best coupled cluster
methods can be prohibitively expensive for systems larger than ten to twenty
non-hydrogen atoms on today's computers. By contrast machine learning methods
offer inexpensive alternatives but are hampered by generalization to molecules
outside the original training set. Here we propose a novel machine learning
feature representation informed by intermediate calculations of atomic chemical
shielding tensors within a molecular environment using an inexpensive quantum
mechanics method, and training it to predict NMR chemical shieldings of a
high-level composite theory that is comparable to CCSD(T) in the complete basis
set limit. The inexpensive shift machine learning (iShiftML) algorithm is
trained through a new progressive active learning workflow that reduces the
total number of expensive calculations required when constructing the dataset,
while allowing the model to continuously improve on data it has never seen.
Furthermore, we show that the error estimations from our model correlate quite
well with actual errors to provide confidence values on new predictions. We
illustrate the predictive capacity of iShiftML across gas phase experimental
chemical shifts for small organic molecules and much larger and more complex
natural products in which we can accurately differentiate between subtle
diastereomers based on chemical shift assignments.
