Universal interatomic potential for perovskite oxides

Abstract

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 parameterized 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 parameterized 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 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 phase transition sequences for several markedly different ferroelectric oxides, including a 6-element ternary solid solution, Pb(In${}_{1/2}$Nb${}_{1/2}$)O${}_3$--Pb(Mg${}_{1/3}$Nb${}_{2/3}$)O${}_3$--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.

arXiv comment18 pages, 4 figures

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Cited by in corpus (5)

• DPA-2: a large atomic model as a multi-task learner
• Finite-temperature properties of antiferroelectric perovskite $\mathrm{PbZr}{\mathrm{O}}_3$ from deep learning interatomic potential
• Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models
• Role of long-range dipolar interactions in the simulation of the properties of polar crystals using effective atomic potentials
• Intrinsic structure of relaxor ferroelectrics from first principles