Despite its potential, accelerated discovery faces several challenges:
Data integration: Combining data from different sources and formats can be complex. Model accuracy: Computational models must be highly accurate to be useful, which requires precise input data and sophisticated algorithms. Scalability: Techniques that work well in the lab may not always scale up effectively for industrial applications. Interdisciplinary collaboration: Successful accelerated discovery often requires collaboration between chemists, materials scientists, and data scientists.
