Validation of computational models is essential to ensure their accuracy and reliability. This is typically done by comparing the computational results with experimental data. Parameters such as reaction energies, activation barriers, and product distributions obtained from simulations are matched against experimental observations. Consistency between the two sets of data confirms the validity of the computational model. Additionally, sensitivity analyses are often performed to understand how variations in input parameters affect the outcomes, ensuring robustness.
