What are the Challenges in Implementing Automated Optimization?
Despite its advantages, automated optimization in nanotechnology faces several challenges:
Data Quality: The accuracy of optimization depends heavily on the quality and quantity of data available. Computational Resources: High-performance computing resources are often required to handle complex simulations and large datasets. Interdisciplinary Knowledge: Effective optimization requires expertise in material science, computational modeling, and machine learning. Validation: Experimental validation can be resource-intensive and requires sophisticated laboratory equipment.
