What challenges do AI and ML face in Nanotechnology?
Despite their potential, AI and ML face several challenges in nanotechnology, such as:
Data quality and quantity: High-quality, extensive datasets are required to train effective AI models, but such datasets are often scarce in nanotechnology. Complexity of nanosystems: The intricate nature of nanoscale interactions can make it difficult for AI models to accurately predict outcomes. Interpretability: Understanding the decision-making process of AI models can be challenging, which may hinder their acceptance in scientific communities. Integration with traditional methods: Combining AI and ML with conventional experimental and theoretical approaches requires careful coordination and validation.
