  Materials characterization remains a significant, time-consuming undertaking.
Generally speaking, spectroscopic techniques are used in conjunction with
empirical and ab-initio calculations in order to elucidate structure. These
experimental and computational methods typically require significant human
input and interpretation, particularly with regards to novel materials.
Recently, the application of data mining and machine learning to problems in
material science have shown great promise in reducing this overhead. In the
work presented here, several aspects of machine learning are explored with
regards to characterizing a model material, titania, using solid-state Nuclear
Magnetic Resonance (NMR). Specifically, a large dataset is generated,
corresponding to NMR $^{47}$Ti spectra, using ab-initio calculations for
generated TiO$_2$ structures. Principal Components Analysis (PCA) reveals that
input spectra may be compressed by more than 90%, before being used for
subsequent machine learning. Two key methods are used to learn the complex
mapping between structural details and input NMR spectra, demonstrating
excellent accuracy when presented with test sample spectra. This work compares
Support Vector Regression (SVR) and Artificial Neural Networks (ANNs), as one
step towards the construction of an expert system for solid state materials
characterization.
