Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection

Symbolic regression offers a promising avenue for describing
the
structure–property relationships of materials with explicit
mathematical expressions, yet it meets challenges when the key variables
are unclear because of the high complexity of the problems. In this
work, we propose to solve the difficulty by automatically searching
for important variables from a large pool of input features. A new
algorithm that integrates symbolic regression with iterative variable
selection (VS) was designed for optimization of the model with a large
amount of input features. Using the recent method SISSO for symbolic
regression and random search for variable selection, we show that
the VS-assisted SISSO (VS–SISSO) can effectively manage even
hundreds of input features that the SISSO alone was computationally
hindered, and it fastly converges to (near) optimal solutions when
the model complexity is not high. The efficiency of this approach
for improving the accuracy of symbolic regression in materials science
was demonstrated in the two showcase applications of learning approximate
equations for the band gap of inorganic halide perovskites and the
stability of single-atom alloy catalysts.