  New crystal structures are frequently derived by performing ionic
substitutions on known crystal structures. These derived structures are then
used in further experimental analysis, or as the initial guess for structural
optimization in electronic structure calculations, both of which usually
require a reasonable guess of the lattice parameters. In this work, we propose
two lattice prediction schemes to improve the initial guess of a candidate
crystal structure. The first scheme relies on a one-to-one mapping of species
in the candidate crystal structure to a known crystal structure, while the
second scheme relies on data-mined minimum atom pair distances to predict the
crystal volume of the candidate crystal structure and does not require a
reference structure. We demonstrate that the two schemes can effectively
predict the volumes within mean absolute errors (MAE) as low as 3.8% and 8.2%.
We also discuss the various factors that may impact the performance of the
schemes. Implementations for both schemes are available in the open-source
pymatgen software.
