While HTCS offers significant advantages, it also presents several challenges:
- Accuracy of Predictions: Ensuring the accuracy of computational predictions is critical. Inaccurate models can lead to false positives or negatives. - Data Quality: High-quality, reliable data is essential for training machine learning models and making accurate predictions. - Computational Resources: HTCS requires substantial computational resources, including high-performance computing facilities. - Integration with Experimental Data: Effective integration of computational predictions with experimental validation is necessary to confirm the performance of identified catalysts.
