Work

Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives

Year: 2024

Type: article

Abstract: 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.

Source: International Journal of Molecular Sciences

DOI: 10.3390/ijms252212368

Authors: Arnav Tripathy, Akshata Y. Patne, Subhra Mohapatra, Shyam S. Mohapatra

Institutions: University of South Florida, James A. Haley Veterans' Hospital

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,296

Funder: U.S. Department of Veterans Affairs