How to Predict the pK _a of Any Compound in Any Solvent

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Solvent Effects

Background:

• Acid-base properties (pKa) are crucial in chemistry, but data in nonaqueous solvents is limited.
• Existing computational methods for predicting pKa in nonaqueous solvents are not universally applicable or simple.
• A general computational procedure for predicting pKa across various solvents is still needed.

Purpose of the Study:

• To develop a simple, general computational procedure for predicting pKa values of molecules in nonaqueous solvents.
• To validate the method by computing proton solvation energy and comparing predicted pKa with experimental data for numerous compounds and solvents.

Main Methods:

• Utilizing experimental pKa of a reference compound in water and standard quantum-chemical calculations.
• Computing proton solvation energy in 39 different solvents.
• Comparing computed pKa for 142 compounds in 12 solvents against experimental values.

Main Results:

• The proton solvation energy computation method proved robust across different computational setups and solvation models.
• Unscaled pKa predictions using implicit solvation models showed significant deviations from experimental data.
• Linear scaling relationships were developed to correct deviations in both protic and aprotic solvents.

Conclusions:

• The developed computational procedure, combined with linear scaling, significantly reduces deviations between predicted and experimental pKa values in nonaqueous solvents.
• The method's efficiency is comparable to pKa prediction accuracy in water.
• This work provides a valuable tool for understanding and predicting acid-base behavior in diverse chemical environments.