A Versatile Method for Accurately Predicting Electronic Absorption Spectra of Tetrapyrrole Macrocycles

Understanding the UV-Vis-NIR electronic spectra of tetrapyrrole macrocycles (porphyrins, chlorins, and bacteriochlorins) is crucial to rational designs of novel functional molecules and materials for energy conversion, catalysis, bio-imaging, and medical applications. Despite this, no methods have been defined to accurately predict the electronic absorption spectra of tetrapyrrole molecules bypassing the expensive and time-consuming TDDFT calculations and enable high throughput screening for thousands of molecules in a short-time period. Using a combination of computational chemistry, Gouterman four-orbital model, and machine learning methods, we show accurate predictions of the electronic absorption spectra of tetrapyrroles bearing various peripheral substituents across the UV-Vis-NIR spectral regions. Optimized AutoGluon pipelines trained on the full data set achieve high predictive fidelity (R² = 0.985 validation, 0.936 test; mean/median errors < 0.1 eV) and robust transferability across different macrocyclic scaffolds. Together, this work establishes an efficient (cost and time), scalable, and mechanistically grounded framework for predicting tetrapyrrole electronic absorption spectra, offering broad utilities in rational designs of tetrapyrrole-based functional materials for real-world applications.