Interpretable machine learning integrated with TD-DFT descriptors and SHAP analysis for predicting the maximum absorption wavelength of azo dyes.

ABSTRACT: The maximum absorption wavelength (λ _max) represents a key property determining the application performance of azo dyes, and accurate prediction of λ _max is of paramount importance for accelerating the rational design of novel dye molecules. Existing prediction models exhibit significant limitations in terms of prediction accuracy and chemical interpretability. In this work, we propose an innovative prediction framework for λ _max of azo dyes by integrating Gaussian Process Regression (GPR) with key molecular descriptors derived from time-dependent density functional theory (TD-DFT) calculations. Results indicate that the coefficient of determination (R ²) for leave-one-out cross-validation (LOOCV) was 0.83, and that fo

SUBMITTER: Fang Y 

PROVIDER: S-EPMC12704590 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature