Predicting Fluorescence Emission Wavelengths and Quantum Yields via Machine Learning

The search for functional fluorescent organic materials can significantly benefit from rapid and accurate predictions of photophysical properties. However, screening large numbers of potential fluorophore molecules in different solvents faces the limitations of quantum mechanical calculations and experimental measurements. In this work, we develop machine learning (ML) algorithms for predicting the fluorescence of a molecule focusing on two target properties: emission wavelengths (WLs) and quantum yield (QYs). For this purpose, we employ the Deep4Chem database containing optical properties of 20,236 combinations of 7,016 chromophores in 365 different solvents. Several chemical descriptors, or features, were selected as inputs for each model, and each molecule was characterized by its SMILES fingerprint. The Shapley Additive explanations (SHAP) technique was used to rationalize the results, showing that the most impactful properties are chromophore-related, as expected from chemical intuition. For the best-performing model, the Random Forest, our results for the test set show a root-mean-square error (RMSE) of 28.8 nm for WL and 0.19 for QY. The developed ML models were used to predict, thus complete, the missing results for the WL and QY target properties in the original Deep4Chem database, resulting in two new databases, one for each property. Testing our ML models for each target property in molecules not included in the Deep4Chem database presented good results.