Universal interatomic potential for perovskite oxides

作者：Jing Wu, Jiyuan Yang, Yuan-Jinsheng Liu, Duo Zhang, Yudi Yang, Yuzhi Zhang, Linfeng Zhang, Shi Liu · 发表于：Physical review. B./Physical review. B · 年份：2023 · DOI：10.1103/physrevb.108.l180104 · 被引用次数：38 · 研究领域：Ferroelectric and Piezoelectric Materials、Machine Learning in Materials Science、Multiferroics and related materials

With their celebrated structural and chemical flexibility, perovskite oxides have served as a highly adaptable material platform for exploring emergent phenomena arising from the interplay between different degrees of freedom. Molecular dynamics (MD) simulations leveraging classical force fields, commonly depicted as parametrized analytical functions, have made significant contributions in elucidating the atomistic dynamics and structural properties of crystalline solids including perovskite oxides. However, the force fields currently available for solids are rather specific and offer limited transferability, making it time-consuming to use MD to study new materials systems since a new force field must be parametrized and tested first. The lack of a generalized force field applicable to a broad spectrum of solid materials hinders the facile deployment of MD in computer-aided materials discovery (CAMD). Here, by utilizing a deep-neural network with a self-attention scheme, we have developed a unified force field (UniPero) that enables MD simulations of perovskite oxides involving 14 metal elements and conceivably their solid solutions with arbitrary compositions. Notably, isobaric-isothermal ensemble MD simulations with this model potential accurately predict the experimental temperature-driven phase transition sequences for several markedly different ferroelectric oxides, including a six-element ternary solid solution $\mathrm{Pb}({\mathrm{In}}_{1/2}{\mathrm{Nb}}_{1/2}){\math...