  Development of new functional ceramics is important for several applications,
including electrochemical batteries and fuel cells. Computational prescreening
and selection of such materials can help discover novel materials but is
challenging due to the high cost of electronic structure calculations which
would be needed to compute the structures and properties of interest such as
the material's stability and ion diffusion properties. The soft bond valence
(SoftBV) approach is attractive for rapid prescreening among multiple
compositions and structures, but the simplicity of the approximation can make
the results inaccurate. We explore the possibility of enhancing the accuracy of
the SoftBV approach when estimating crystal structures by adapting the
parameters of the approximation to the chemical composition. Specifically, on
the examples of perovskite- and spinel-type oxides that have been proposed as
promising solid-state ionic conductors, the screening factor, an independent
parameter of the SoftBV approximation, is modeled using linear and non-linear
methods as a function of descriptors of chemical composition. We find that
making the screening factor a function of composition can noticeably improve
the ability of SoftBV to correctly model structures, in particular new,
putative crystal structures whose structural parameters are yet unknown. We
also analyze the relative importance of nonlinearity and coupling in improving
the model and find that while the quality of the model is improved by including
nonlinearity, coupling is relatively unimportant. While using a neural network
showed no improvement over linear regression, the recently proposed GPR-NN
method that is a hybrid between a single hidden layer neural network and kernel
regression showed substantial improvement, enabling the prediction of
structural parameters of new ceramics with accuracy on the order of 1%.
