PW-SMD: A Plane-Wave Implicit Solvation Model Based on Electron Density for Surface Chemistry and Crystalline Systems in Aqueous Solution

Electron density-based implicit solvation
models are
a class of
techniques for quantifying solvation effects and calculating free
energies of solvation without an explicit representation of solvent
molecules. Integral to the accuracy of solvation modeling is the proper
definition of the solvation shell separating the solute molecule from
the solvent environment, allowing for a physical partitioning of the
free energies of solvation. Unlike state-of-the-art implicit solvation
models for molecular quantum chemistry calculations, e.g., the solvation model based on solute electron
density (SMD), solvation models for systems under periodic boundary
conditions with plane-wave (PW) basis sets have been limited in their
accuracy. Furthermore, a unified implicit solvation model with both
homogeneous solution-phase and heterogeneous interfacial structures
treated on equal footing is needed. In order to address this challenge,
we developed a high-accuracy solvation model for periodic PW calculations
that is applicable to molecular, ionic, interfacial, and bulk-phase
chemistry. Our model, PW-SMD, is an extension of the SMD molecular
solvation model to periodic systems in water. The free energy of solvation
is partitioned into the electrostatic and cavity–dispersion–solvent
structure (CDS) contributions. The electrostatic contributions of
the solvation shell surrounding solute structures are parametrized
based on their geometric and physical properties. In addition, the
nonelectrostatic contribution to the solvation energy is accounted
for by extending the CDS formalism of SMD to incorporate periodic
boundary conditions. We validate the accuracy and robustness of our
solvation model by comparing predicted solvation free energies against
experimental data for molecular and ionic systems, carved-cluster
composite energetic models of solvated reaction energies and barriers
on surface systems, and deep-learning-accelerated ab initio molecular dynamics (AIMD). Our developed periodic implicit solvation
model shows significantly improved accuracy compared to previous work
(namely, solvation models in aqueous solution) and can be applied
to simulate solvent effects in a wide range of surface and crystalline
materials.