Informática de materiales en el estudio de propiedades físicas de nanotubos de carbono
DOI:
https://doi.org/10.5281/zenodo.14193613Keywords:
Machine learning, artificial intelligence, nanotechnology, carbon-based nanoestructuresAbstract
Materials informatics (MI) constitutes a new paradigm in the study of nanomaterials, where machine learning (ML) approaches are implemented in nanotechnology. IM is a powerful tool in the study of carbon nanotubes (CNTs), which have exceptional physical properties, leading them to be used in optics, chemistry, computing, and medicine, among other areas. This work describes the most recent research in IM applied to CNTs. The ML algorithms used in the NTC study, such as artificial neural networks, decision trees, and support vector machines, are explained in detail. Likewise, studies are presented where computational simulation approaches have been useful in developing predictive models of CNT properties and behaviors. Open research questions are identified in the analysis of physical properties such as thermal conductivity and vibrational modes of CNT, where IM could support their greater understanding, helping in the development of nanosensors. Finally, IM can help in reducing time and resource costs in the characterization of the physical properties of nanomaterials.
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