Universal interatomic potential for perovskite oxides

钙钛矿(结构) 力场(虚构) 原子间势 分子动力学 材料科学 固溶体 可转让性 物理 化学物理 结晶学 计算机科学 化学 计算化学 机器学习 量子力学 罗伊特 冶金
作者
Jing Wu,Jiyuan Yang,Yuan-Jinsheng Liu,Duo Zhang,Yudi Yang,Yuzhi Zhang,Linfeng Zhang,Shi Liu
出处
期刊:Physical review [American Physical Society]
卷期号:108 (18) 被引量:35
标识
DOI:10.1103/physrevb.108.l180104
摘要

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 $\mathrm{Pb}({\mathrm{In}}_{1/2}{\mathrm{Nb}}_{1/2}){\mathrm{O}}_{3}\text{--}\mathrm{Pb}({\mathrm{Mg}}_{1/3}{\mathrm{Nb}}_{2/3}){\mathrm{O}}_{3}\text{--}{\mathrm{PbTiO}}_{3}$. We believe the universal interatomic potential along with the training database, proposed regression tests, and the auto-testing workflow, all released publicly, will pave the way for a systematic improvement and extension of a unified force field for solids, potentially heralding a new era in CAMD.
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