Mapping Correlated Environmental Landscapes in the Fluorescence Response of a Pyrenylated Charge‐Transfer Probe via Data‐Driven Nonlinear Regression

ABSTRACT Here, we report the design and detailed photophysical investigation of a pyrene aniline‐based donor–acceptor probe (compound 1) that displays pronounced sensitivity to multiple environmental factors. Steady‐state and time‐resolved fluorescence studies reveal strong solvatochromism, viscosity‐enhanced emission following the Förster Hoffmann relationship over a broad range (≈1.9–100 cP), concentration‐dependent aggregation effects, and thermally activated fluorescence quenching without changes in the emissive electronic state. Dynamic light scattering and density functional theory calculations establish a sterically twisted donor–acceptor architecture that supports intramolecular charge transfer while remaining highly responsive to microenvironmental constraints. Building on this experimentally validated framework, machine learning (ML) was employed as a computational extension of fluorescence spectroscopy to reconstruct continuous fluorescence–environment relationships from discrete datasets. Nonlinear regression models, particularly random forest and Gaussian process regression (GPR), accurately capture parameter‐specific response topologies, outperforming linear approaches. Correlated viscosity with temperature and viscosity with concentration intensity maps reveal regime‐dependent fluorescence behavior that cannot be inferred from single‐parameter trends alone. Point‐wise experimental validation confirms agreement in relative intensity ordering under combined parameter variation. Together, this work demonstrates a data‐curated strategy for integrating supramolecular photophysics with ML, enabling physically consistent interpretation of complex fluorescence responses without extrapolation beyond experimental control.