Background : Deep neural networks increasingly power language, vision, and decision systems, yet many deployments require explanations that are faithful, compositional, and governance-ready. Symbolic techniques promise these properties, but the literature mixes post-hoc extraction, knowledge injection, and intrinsically hybrid designs without a unifying view. Objectives : We provide a systematic …
Scientific Reports, Published online: 19 July 2026; doi:10.1038/s41598-026-62721-x Symbolic and domain-generalized machine learning for interpretable solubility modeling in supercritical CO₂
