FerroAI: a deep learning model for predicting phase diagrams of ferroelectric materials
Chenbo Zhang, Xian Chen
Abstract
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 2838 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 11,051. These results establish FerroAI as a powerful tool for phase diagram construction, guiding the design of high-performance ferroelectric materials.