Density functional theory (DFT) study of water autoionization in solvated clusters

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Materials Science

Background:

• Accurate modeling of water autoionization and solvation energetics is crucial for understanding chemical processes.
• Previous studies often assumed exponential convergence in thermodynamic calculations, potentially leading to inaccuracies.
• The cluster-continuum method offers a promising approach for simulating complex aqueous systems.

Purpose of the Study:

• To determine the minimal basis set and cluster size for accurate density functional theory (DFT) modeling of water autoionization.
• To establish the minimum number of explicit water molecules needed to accurately represent solvation energetics.
• To investigate the convergence behavior of water autoionization thermodynamics.

Main Methods:

• Implementation of a cluster-continuum method using density functional theory (DFT).
• Modeling of water clusters and charged species derived from water.
• Utilizing RPBE-D3 and ωB97X-D exchange-correlation functionals with the 6-311+G** basis set.

Main Results:

• Water autoionization thermodynamics converge via a modified power law, not exponentially, enabling accurate Gibbs energy calculations with smaller clusters.
• The n=21 cluster size is identified as the first to effectively mimic bulk water, capturing first and second solvation shell influences.
• Clusters with n ≥ 21 yield good approximations of the free energy change at 298 K, despite limitations in individual enthalpy/entropy modeling by the functionals.

Conclusions:

• The study provides critical insights into the computational requirements for accurate water autoionization and solvation modeling.
• The findings challenge previous assumptions about convergence rates, offering a more realistic understanding of thermodynamic behavior.
• The identified n=21 cluster size serves as a benchmark for future DFT studies on water energetics.