The multicomponent oxide solid solution is a versatile platform to tune the
delicate balance between competing spin, charge, orbital, and lattice degrees
of freedom for materials design and discovery. The development of
compositionally complex oxides with superior functional properties has been
largely empirical and serendipitous, in part due to the exceedingly complex
chemistry and structure of solid solutions that span a range of length scales.
The classical molecular dynamics (MD), as a powerful statistical method to
investigate materials properties over large spatial and temporal scales, often
plays a secondary role in computer-aided materials discovery because of the
limited availability and accuracy of classical force fields. Here, we introduce
the strategy of ``modular developing deep potential" (ModDP) that enables a
systematic development and improvement of deep neural network-based model
potential, termed as deep potential, for complex solid solutions with minimum
human intervention. The converged training database associated with an
end-member material is treated as an independent module and is reused to train
the deep potential of solid solutions via a concurrent learning procedure. We
apply ModDP to obtain classical force fields of two technologically important
solid solutions, PbxβSr1βxβTiO3β and HfxβZr1βxβO2β. For both
materials systems, a single model potential is capable of predicting various
properties of solid solutions including temperature-driven and
composition-driven phase transitions over a wide range of compositions. In
particular, the deep potential of PbxβSr1βxβTiO3β reproduces a few
known topological textures such as polar vortex lattice and electric dipole
waves in PbTiO3β/SrTiO3β superlattices, paving the way for MD
investigations on the dynamics of topological structures in response to
external stimuli.Comment: 32 pages, 9 figure