Predicting Fluorescence Emission Wavelengths and Quantum Yields via Machine Learning.

ABSTRACT: The search for functional fluorescent organic materials can significantly benefit from the rapid and accurate predictions of photophysical properties. However, screening large numbers of potential fluorophore molecules in different solvents faces 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 yields (QYs). For this purpose, we employ the Deep4Chem database which contains the 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

SUBMITTER: Souza RC 

PROVIDER: S-EPMC12004507 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature