Despite its advantages, computational design faces several challenges:
Accuracy: The accuracy of computational predictions depends on the quality of the theoretical models and the computational methods used. Scalability: High-level quantum mechanical calculations can be computationally intensive, limiting their application to large systems. Data Availability: The success of machine learning models depends on the availability of high-quality experimental data for training. Integration: Integrating computational predictions with experimental validation remains a complex task that requires interdisciplinary collaboration.
