Automated Molecular Cluster Growing for Explicit Solvation by Efficient Force Field and Tight Binding Methods

An automated and
broadly applicable workflow for the description
of solvation effects in an explicit manner is introduced. This method,
termed quantum cluster growth (QCG), is based on the semiempirical
GFN2-xTB/GFN-FF methods, enabling efficient geometry optimizations
and MD simulations. Fast structure generation is provided using the
intermolecular force field xTB-IFF. Additionally, the approach uses
an efficient implicit solvation model for the electrostatic embedding
of the growing clusters. The novel QCG procedure presents a robust
cluster generation tool for subsequent application of higher-level
(e.g., DFT) methods to study solvation effects on molecular geometries
explicitly or to average spectroscopic properties over cluster ensembles.
Furthermore, the computation of the solvation free energy with a supermolecular
approach can be carried out with QCG. The underlying growing process
is physically motivated by computing the leading-order solute–solvent
interactions first and can account for conformational and chemical
changes due to solvation for low-energy barrier processes. The conformational
space is explored with the NCI–MTD algorithm as implemented
in the CREST program, using a combination of metadynamics and MD simulations.
QCG with GFN2-xTB yields realistic solution geometries and reasonable
solvation free energies for various systems without introducing many
empirical parameters. Computed IR spectra of some solutes with QCG
show a better match to the experimental data compared to well-established
implicit solvation models.