What are the Challenges in Integrating Machine Learning with Nanotechnology?
Despite its potential, integrating ML with nanotechnology presents several challenges:
1. Data Quality and Quantity: High-quality and comprehensive datasets are essential for training accurate ML models. In nanotechnology, obtaining such data can be difficult due to the complexity and variability of nanomaterials. 2. Interdisciplinary Knowledge: Effective application of ML in nanotechnology requires knowledge in both domains, which can be a barrier for researchers specializing in only one field. 3. Computational Resources: Training complex ML models, especially [deep learning]() networks, requires significant computational power, which can be a limitation for some research facilities.
