What are the Challenges in Data-Driven Nanotechnology?
Several challenges need to be addressed to fully realize the potential of data-driven approaches in nanotechnology:
1. Data Quality: Ensuring the accuracy, consistency, and reliability of data is critical. 2. Data Integration: Combining datasets from different sources and formats can be complex. 3. Computational Resources: High-performance computing is often required for data analysis and simulations. 4. Interdisciplinary Collaboration: Effective collaboration between material scientists, data scientists, and engineers is essential.
