  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.
