Benchmarking First-Principles Reaction Equilibrium Composition Prediction.

Area of Science:

• Computational Chemistry
• Chemical Thermodynamics
• Materials Science

Background:

• Accurate thermochemical properties are vital for predicting chemical reaction equilibrium.
• Experimental data for novel chemical processes is often unavailable.
• First-principles methods like Density Functional Theory (DFT) offer a computational alternative.

Purpose of the Study:

• To benchmark the accuracy of DFT in predicting temperature-dependent chemical reaction equilibrium compositions.
• To assess DFT's reliability for designing new chemical synthesis routes.
• To identify sources of error in DFT-based thermochemical predictions.

Main Methods:

• Collected experimental thermochemical data for 117 molecules.
• Constructed 2648 chemical reactions from the experimental data.
• Benchmarked DFT calculations against experimental data using 6 exchange-correlation functionals and 3 basis sets.

Main Results:

• DFT correctly predicted equilibrium compositions for over 90% of reactions without significant temperature dependence below 1000 K.
• DFT generally captured the qualitative behavior of temperature-dependent equilibrium compositions.
• Errors in DFT predictions were attributed equally to vibrational spectrum inaccuracies and electronic ground state energy errors.

Conclusions:

• DFT is a reliable tool for predicting chemical reaction equilibrium compositions, crucial for chemical synthesis design.
• The accuracy of DFT predictions is high, especially for reactions without significant temperature dependence.
• Addressing errors in vibrational spectra and electronic ground state energies will further enhance DFT's predictive power.