Accelerated search for new ferroelectric materials

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 polar crystal structures, 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, nonpolar 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.

Automated summary

We developed a combined machine learning and high-throughput DFT framework to speed up the search for new ferroelectric materials. The framework predicts potential ferroelectric compounds based on elemental composition. Machine learning algorithms predict stable and insulating stoichiometries with polar crystal structures, and a classification model predicts their point groups. High-throughput DFT calculations find the lowest-energy crystal structure within each point group. Nonpolar parent structures are identified using group theory considerations, and the spontaneous polarization values are calculated using DFT. This method provides a powerful tool to explore new ferroelectric materials beyond existing databases.