Dresden 2026 – wissenschaftliches Programm

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MM: Fachverband Metall- und Materialphysik

MM 5: Topical Session: Physics-driven Artificial Intelligence for Materials I

MM 5.6: Vortrag

Montag, 9. März 2026, 12:15–12:30, SCH/A251

Unveiling the Core of Materials Properties via SISSO and Sensitivity Analysis: Use-case Demonstration for Perovskites — •Lucas Foppa and Matthias Scheffler — The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society, Berlin, Germany.

Interpretable AI can help reveal the physical principles governing intricate material properties and functions. In particular, the sure-independence screening and sparsifying operator (SISSO) symbolic-regression approach identifies analytical expressions correlating a target materials performance to a small set of physical descriptive parameters, termed materials genes, selected from a vast pool of primary features. However, the identified genes influence the SISSO models to different degrees. Here, we use the gradient-based partial-effect sensitivity analysis to pinpoint the most influential genes, thus enhancing SISSO's interpretability and enabling deeper physical insights. This analysis also highlights that different combinations of genes can yield equally accurate descriptions of the correlation. The approach is demonstrated for the bulk properties of perovskites.

Keywords: symbolic regression; interpretable artificial intelligence; perovskites; sensitivity analysis