To address these challenges, we set out to exploit advances in quantum chemistry, high-performance computing, and automation in developing a large dataset of QM computed values to train an ML model. A principal advantage of this approach is that DFT-based predictions of chemical shifts can be mapped to the responsible atom in a high-throughput fashion with complete reliability, avoiding incomplete or erroneous assignments and the need for manual intervention. Datasets containing 100,000 C and 1 H chemical shifts are readily attainable via automation (see below), and the conformational dependence of chemical shifts can be effectively learned by the inclusion of different molecular geometries.