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 enable high throughput screening for a large body of tetrapyrrole molecules ([Formula: see text]) in a short-time period and accurately predict their electronic absorption spectra. Traditional time dependent-density functional theory (TD-DFT) method is cheap for single molecule simulation, but it can be expensive and time-consuming for massive numbers of molecules . 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 generalized well across the metal-free porphyrin, chlorin, and bacteriochlorin scaffolds represented in this study. 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.