What are Some Challenges in Using Computational Data?
While computational data offers numerous advantages, several challenges persist:
Accuracy: Quantum mechanical methods like DFT can sometimes yield inaccurate results due to approximations in exchange-correlation functionals. Computational Cost: High-level calculations, especially for large systems, can be computationally expensive and time-consuming. Complexity: Modeling real catalytic systems often requires considering a multitude of factors, including solvent effects, temperature, and pressure, which complicates simulations. Data Integration: Integrating computational data with experimental findings and ensuring consistency across different types of data can be challenging.
