Systematic Error Estimation for Chemical Reaction Energies.

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Chemical Reactivity

Background:

• Accurate relative energies of intermediates and transition states are crucial for understanding chemical reactivity.
• Density functional theory (DFT) is computationally feasible for complex systems but often lacks accuracy, particularly for transition metal compounds.
• Existing DFT methods struggle with system-dependent parameterization, limiting their predictive power.

Purpose of the Study:

• To develop a Bayesian framework for density functional theory (DFT) to provide reliable error estimation for calculated properties.
• To introduce a system-focused reparameterization approach for DFT functionals to enhance accuracy for specific chemical systems.
• To enable the calculation of reaction energies with reliable confidence intervals.

Main Methods:

• Developed a Bayesian framework to quantify uncertainties in DFT calculations.
• Implemented a system-focused reparameterization strategy for DFT functionals, acknowledging and leveraging system dependence.
• Applied the framework to reparameterize a physically based functional for the specific case of catalytic nitrogen fixation.

Main Results:

• The Bayesian DFT framework provides stochastically meaningful error estimates for calculated properties.
• System-focused reparameterization yields functionals with improved accuracy for reaction energies in the target chemical system.
• Demonstrated reliable confidence intervals for reaction energies in the context of catalytic nitrogen fixation.

Conclusions:

• The proposed Bayesian framework offers a robust method for error estimation in DFT calculations.
• System-dependent reparameterization, when implemented within a Bayesian approach, is non-arbitrary and enhances predictive accuracy.
• This approach significantly improves the reliability of theoretical studies for complex chemical processes, such as catalytic nitrogen fixation.