Congratulations to our PhD student Patrizia Mazzeo for her publication entitled "Multiscale Machine Learning Prediction of Infrared Spectra of Solvated Molecules" in the Journal of Chemical Theory and Computation. The work introduces a multiscale machine-learning molecular dynamics (MD) strategy for simulating infrared spectra of solvated molecules. This approach integrates an efficient sampling of environmental configurations with a hierarchical model that predicts forces and dipole moments as analytical derivatives of the energy, allowing IR spectra simulations from MD trajectories.
Solvent effects are incorporated through a molecular mechanics (MM) representation of the environment embedded within the ML description of the solute. Applied to representative biorelated systems, the resulting ML/MM framework reproduces experimental spectra with high fidelity and accurately captures solvent-driven vibrational shifts. This approach provides a computationally efficient and robust route for describing solvent effects in vibrational spectroscopy.
To know more, check out the work at https://doi.org/10.1021/acs.jctc.5c01959
