More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins

Abstract

The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.26 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.

Synthesis, Structure, and Spectral-Luminescent Properties of Peripherally Fluorinated Mg(II) and Zn(II) Octaphenyltetraazaporphyrins

“…We previously developed a QSPR mathematical model for the prediction of the absorption wavelength of porphyrins and their analogues [ 27 ] on the online portal Online Chemical Database and Modeling Environment (Ochem) [ 28 , 29 ] using five-fold cross-validation [ 30 ], the random forest regression (RFR) machine learning method [ 31 ] and a consensus model integrating models with several descriptors. We used it to predict the spectral properties of the compounds described in this work, including those from [ 19 , 20 ].…”

Accurate prediction of 11B NMR chemical shift of BODIPYs via machine learning

“…39 At the first stage, the Deep Neural Network (DNN), 41 Random Forests Regression (RFR), 42 eXtreme Gradient Boosting (XGBoost), 43 Transformer Convolutional Neural Fingerprint (Transformer-CNF), 44 Transformer Convolutional Neural Networks (Transformer-CNN) 45 and SchNet 46 were chosen as ML algorithms. Previously, these methods showed a good quality of predicting the spectral properties (absorption maximum wavelengths and molar absorption coefficient) of dipyrromethene compounds including BODIPYs [47][48][49][50] and thermal properties of ionic liquids. 51,52 RFR is the most flexible and easy-to-use algorithm based on classification and regression trees (CART).…”