  A neural network is used to train, predict, and evaluate a model to calculate
the energies of 3-dimensional systems composed of Ti and O atoms. Python
classes are implemented to quantify atomic interactions through symmetry
functions and to specify prediction algorithms. The hyperparameters of the
model are optimised by minimising validation RMSE, which then produced a model
that is accurate to within 100 eV. The model could be improved by proper
testing of symmetry function calculations and addressing properties of features
and targets.
