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• W4378214624 abstract "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." @default.
• W4378214624 created "2023-05-26" @default.
• W4378214624 creator A5010635053 @default.
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• W4378214624 date "2023-05-25" @default.
• W4378214624 modified "2023-10-10" @default.
• W4378214624 title "Accelerated search for new ferroelectric materials" @default.
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• W4378214624 doi "https://doi.org/10.1103/physrevresearch.5.023122" @default.
• W4378214624 hasPublicationYear "2023" @default.
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