Deep Learning Approach to Material Properties
In this thesis, we consider a deep learning approach to predict material properties. Primarily we study artificial neural networks (ANN), which predict the energy distance to the convex hull (measure of stability) of perovskites. Further, we explore if the networks can be generalised to predict band gaps and unit cell volume. We also demonstrate total energy calculations using density functional t
