A reliable and efficient first principles-based method for predicting pK(a) values. 2. Organic acids.

Area of Science:

• Computational Chemistry
• Physical Chemistry
• Chemical Thermodynamics

Background:

• Accurate prediction of acid dissociation constants (pK(a)) is crucial in chemistry.
• Previous methods for large-scale pK(a) calculations often involve a trade-off between accuracy and computational cost.

Purpose of the Study:

• To develop and validate an efficient computational protocol for predicting pK(a) values.
• To derive empirical parameters for various acidic functional groups based on first principles calculations.

Main Methods:

• Utilized a linear regression fit to deprotonation energies for pK(a) prediction.
• Derived alpha(f) and beta(f) parameters for functional groups like -COOH, -OH, -SH, and -NHOH.
• Validated the protocol against a dataset of 449 experimental pK(a) values.

Main Results:

• The recommended method, using five functional group families and 10 empirical parameters, achieved mean absolute deviations of 0.4 pK(a) units or less.
• Over 90% of calculated pK(a) values were within +/-0.8 pK(a) units of experimental values.
• Subgroup analysis, e.g., for monocarboxylic acids, yielded higher accuracy with more parameters.

Conclusions:

• The developed protocol provides a computationally efficient and accurate approach for large-scale pK(a) determination.
• Results compare favorably to existing models, offering a better balance of accuracy and computational cost.
• The protocol is suitable for predicting pK(a) values across a range of acidic functional groups.