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With their celebrated structural and chemical flexibility, perovskite oxideshave served as a highly adaptable material platform for exploring emergentphenomena arising from the interplay between different degrees of freedom.Molecular dynamics (MD) simulations leveraging classical force fields, commonlydepicted as parameterized analytical functions, have made significantcontributions in elucidating the atomistic dynamics and structural propertiesof crystalline solids including perovskite oxides. However, the force fieldscurrently available for solids are rather specific and offer limitedtransferability, making it time-consuming to use MD to study new materialssystems since a new force field must be parameterized and tested first. Thelack of a generalized force field applicable to a broad spectrum of solidmaterials hinders the facile deployment of MD in computer-aided materialsdiscovery (CAMD). Here, by utilizing a deep-neural network with aself-attention scheme, we have developed a unified force field that enables MDsimulations of perovskite oxides involving 14 metal elements and conceivablytheir solid solutions with arbitrary compositions. Notably, isobaric-isothermalensemble MD simulations with this model potential accurately predict theexperimental phase transition sequences for several markedly differentferroelectric 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$_3$. Webelieve the universal interatomic potential along with the training database,proposed regression tests, and the auto-testing workflow, all releasedpublicly, will pave the way for a systematic improvement and extension of aunified force field for solids, potentially heralding a new era in CAMD.

Universal interatomic potential for perovskite oxides