Fig. 2: Machine learning model architecture.
From: Machine learning-based prediction of polaron-vacancy patterns on the TiO2(110) surface
a A defect structure consisting of oxygen vacancies and polarons in a supercell. b The supercell is converted into a discretized grid, where each cell encodes whether it contains a defect/polaron. c Smearing of the one-hot encoding. d The supercell is partitioned into the local environment of each defect. e The local environment descriptors are fed through a feed-forward neural network to predict the energy contribution of each defect. The sum of the individual defect contributions gives the total energy of the system.
