Congratulations to Amanda Arcidiacono, Dr. Edoardo Cignoni and Patrizia Mazzeo for the publication of their work entitled 'Predicting Slvatochromism of Chromophores in Proteins through QM/MM and Machine Learning', on the Journal of Physiscal Chemistry A. In this work the authors unravel the complexity of solvatochromism in one of the most abundant classes of natural chromophores, the Carotenoids.
With Machine Learning regression strategies and dimensionality reduction techniques, it is indeed possible to both reproduce the excited states energy of these molecules, and to provide physical interpretations to the changes induced by external perturbations, such as the influence of a protein environment.
The work is available at https://pubs.acs.org/doi/full/10.1021/acs.jpca.4c00249
