Kavli Affiliate: Xian Chen
| First 5 Authors: Chenbo Zhang, Xian Chen, , ,
| Summary:
Composition-temperature phase diagrams are crucial for designing
ferroelectric materials, however predicting them accurately remains challenging
due to limited phase transformation data and the constraints of conventional
methods. Here, we utilize natural language processing (NLP) to text-mine 41,597
research articles, compiling a dataset of 2,838 phase transformations across
846 ferroelectric materials. Leveraging this dataset, we develop FerroAI, a
deep learning model for phase diagram prediction. FerroAI successfully predicts
phase boundaries and transformations among different crystal symmetries in
Ce/Zr co-doped BaTiO$3$ (BT)-$x$Ba${0.7}$Ca$_{0.3}$TiO$_3$ (BCT). It also
identifies a morphotropic phase boundary in Zr/Hf co-doped BT-$x$BCT at $x =
0.3$, guiding the discovery of a new ferroelectric material with an
experimentally measured dielectric constant of 9535. These results establish
FerroAI as a powerful tool for phase diagram construction, guiding the design
of high-performance ferroelectric materials.
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