Multivariate Analysis of Linear and Nonlinear Optical Properties in Purine Derivatives: A Predictive Framework from One-Photon Absorption Spectra

High Resolution Image Download MS PowerPoint Slide The rational design of fluorescent organic molecules is central to the development of advanced linear and nonlinear photonic materials. Purine-based compounds have emerged as promise candidates for several photonics applications due to their structural similarity to biological nucleobases synthetic versatility and favorable photophysical properties. However, their optical characterization typically generates large and complex data sets that are difficult to interpret, particularly when multiple compounds are analyzed simultaneously. Here, we apply principal component analysis (PCA) to a series of purine derivatives to systematically investigate the relationships between molecular descriptors and photophysical performance. The PCA model applied in the optical properties of the set captures 76.8% of the total variance within the first two principal components, enabling clear clustering of molecules according to their electronic structure. Importantly, by applying PCA directly to one- and two-photon absorption spectra, we achieve effective spectral deconvolution with 91.87% and 94.51%, respectively, isolating contributions associated with intensity, spectral shifts, and bandwidth. The robustness of this approach is validated through accurate spectral reconstruction. To extend the analysis toward predictive modeling, multiple linear regression (MLR) was employed to correlate PCA-derived features from one-photon absorption data with the transition dipole moment ( μ 01 ). The proposed PCA-MLR framework effectively captures the intrinsic relationships within the spectra of the studied group, minimizing the need for extensive experimental trials. The resulting model exhibits excellent predictive performance ( R 2 = 0.9728) and accurately estimating the μ 01 = 7.07D of an external validation molecule with a deviation of approximately 2.5%. Overall, this PCA-MLR framework provides a powerful and efficient strategy for interpreting complex photophysical data sets and accelerating the design and optimization of organic molecules for linear and nonlinear photonic applications.