  We report the development of a combined machine-learning and high-throughput
density functional theory (DFT) framework to accelerate the search for new
ferroelectric materials. The framework can predict potential ferroelectric
compounds using only elemental composition as input. A series of
machine-learning algorithms initially predict the possible stable and
insulating stoichiometries with a polar crystal structure, necessary for
ferroelectricity, within a given chemical composition space. A classification
model then predicts the point groups of these stoichiometries. A subsequent
series of high-throughput DFT calculations finds the lowest energy crystal
structure within the point group. As a final step, non-polar parent structures
are identified using group theory considerations, and the values of the
spontaneous polarization are calculated using DFT. By predicting the crystal
structures and the polarization values, this method provides a powerful tool to
explore new ferroelectric materials beyond those in existing databases.
