Artificial Intelligence in Biomedical and Photonic Nanomaterials: Current Applications and Future Outlook

Artificial intelligence (AI) has emerged as a pivotal force in the evolution of nanomaterial design, significantly modifying conventional methodologies through data-centric predictive modeling. This review delineates the methodologies by which AI, notably through machine learning and deep learning paradigms, is harnessed to expedite the identification and enhancement of nanomaterials. It elucidates the merits of AI-facilitated techniques as opposed to traditional experimental and computational methodologies, particularly in terms of diminishing temporal and financial expenditures while augmenting predictive precision. Significant applications are examined, including drug delivery systems, nanophotonics, quantum dots, and energy storage mechanisms. The article further articulates principal challenges such as data constraints, algorithmic biases, and the imperative for empirical validation, while underscoring potential remedial strategies such as explainable AI, automated laboratories, and amalgamated physics-informed models. Anticipating future developments, the confluence of AI with quantum computing and sustainable material innovation is projected to catalyze a paradigm shift in the forthcoming generation of nanotechnologies.

Keywords:

Artificial Intelligence , Nanomaterials , Predictive Modeling , Machine Learning , Deep Learning , Big Data , Nanotechnology