How SISSO-derived materials genes shape materials properties: An analytical sensitivity analysis

Interpretable AI can reveal physical principles governing intricate materials properties by uncovering explicit relationships between physical parameters and target properties. The sure-independence screening and sparsifying operator (SISSO) symbolic regression approach identifies analytical expressions that correlate a target property with a small set of parameters, termed , selected from a large pool of candidates. However, multiple gene combinations can yield equally accurate SISSO models, with individual genes contributing with different weights. Here, we establish a derivative-based sensitivity analysis that enhances interpretability, thereby enabling deeper physical insight. This analysis also reveals how distinct gene combinations encode equivalent information and identifies valence orbital radii, nuclear charges, and their products as the key quantities governing the equilibrium lattice constant of perovskites.