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

Area of Science:

• Computational Chemistry
• Physical Chemistry
• Chemical Physics

Background:

• Accurately modeling solvation effects is crucial for understanding molecular behavior in solution.
• Existing methods often rely on implicit solvation models or computationally expensive explicit simulations.

Purpose of the Study:

• To introduce an automated and broadly applicable workflow, Quantum Cluster Growth (QCG), for explicitly describing solvation effects.
• To enable efficient geometry optimizations, molecular dynamics (MD) simulations, and computation of solvation free energies.

Main Methods:

• The QCG workflow utilizes semiempirical GFN2-xTB/GFN-FF methods and the xTB-IFF for fast structure generation.
• It incorporates an implicit solvation model for electrostatic embedding and the NCI-MTD algorithm for conformational space exploration.
• The method is physically motivated by prioritizing leading-order solute-solvent interactions.

Main Results:

• QCG with GFN2-xTB provides realistic solution geometries and reasonable solvation free energies without extensive empirical parameters.
• Computed IR spectra using QCG show improved agreement with experimental data compared to implicit solvation models.
• The workflow successfully accounts for conformational and chemical changes induced by solvation.

Conclusions:

• QCG is a robust tool for generating molecular clusters to study explicit solvation effects.
• It facilitates the application of higher-level quantum chemical methods (e.g., DFT) for detailed analysis.
• The method offers a promising alternative for accurate and efficient solvation modeling.