Congratulations to our PhD students Patrizia Mazzeo and Amanda Arcidiacono for their publication entitled "Electrostatic embedding machine learning for ground and excited state molecular dynamics of solvated molecules" on the RSC Digital Discovery Journal. In this work, the authors propose a machine-learning (ML) based approach to study the dynamics of solvated molecules on the ground- and excited-state potential energy surfaces.
They also built a socket-based interface of the ML machinery with AMBER to run ML/MM molecular dynamics simulations. Their ML/MM approach accurately reproduced the solvent effects on proton transfer dynamics of 3-hydroxyflavone in two different solvents.
The work is available at https://doi.org/10.1039/D4DD00295D
