  Symbolic regression identifies key physical parameters describing materials
properties by uncovering correlations as nonlinear analytical expressions.
However, the pool of expressions grows rapidly with complexity, compromising
its efficiency. We tackle this challenge by a hierarchical approach: identified
expressions are used as input parameters for obtaining more complex
expressions. Crucially, this framework can transfer knowledge among properties,
highlighting physical relationships. We demonstrate this strategy by using the
Sure-Independence-Screening-and-Sparsifying-Operator (SISSO) approach to
identify expressions correlated with the lattice constant and cohesive energy,
which are then used to model the bulk modulus of ABO3 perovskites.
