Center for Research and Education in Nanobioengineering, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
Graduate Programs, Taneja College of Pharmacy, MDC30, 12908 USF Health Drive, Tampa, FL 33612, USA.
Int J Mol Sci. 2024 Nov 18;25(22):12368. doi: 10.3390/ijms252212368.
Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, and data processing. ML enhances nanotechnology by facilitating the processing of dataset in nanomaterial synthesis, characterization, and optimization of nanoscale properties. Conversely, nanotechnology improves the speed and efficiency of computing power, which is crucial for ML algorithms. Although the capabilities of nanotechnology and ML are still in their infancy, a review of the research literature provides insights into the exciting frontiers of these fields and suggests that their integration can be transformative. Future research directions include developing tools for manipulating nanomaterials and ensuring ethical and unbiased data collection for ML models. This review emphasizes the importance of the coevolution of these technologies and their mutual reinforcement to advance scientific and societal goals.
纳米技术和机器学习（ML）是迅速发展的领域，在医学、材料科学、计算机工程和数据处理等领域有许多实际应用。ML 通过促进纳米材料合成、特性描述和纳米级特性优化中数据集的处理，增强了纳米技术。相反，纳米技术提高了计算能力的速度和效率，这对 ML 算法至关重要。尽管纳米技术和 ML 的能力仍处于起步阶段，但对研究文献的回顾提供了对这些领域令人兴奋的前沿的深入了解，并表明它们的整合具有变革性。未来的研究方向包括开发用于操作纳米材料的工具和确保 ML 模型进行道德和无偏的数据收集。本综述强调了这些技术的共同发展及其相互加强以推进科学和社会目标的重要性。
