Resumen
Las nanoestructuras base carbono han sido objeto de un creciente interés debido a sus notables propiedades físicas y su potencial para aplicaciones en una amplia gama de campos, desde la electrónica hasta la medicina. Sin embargo, comprender y caracterizar completamente estas estructuras a nivel atómico es un desafío complejo debido a su naturaleza intrínsecamente multidimensional y la enorme cantidad de datos involucrados. En este artículo, exploramos cómo el aprendizaje automático, una rama de la inteligencia artificial, ha revolucionado el estudio y la caracterización de las propiedades físicas de nanoestructuras base carbono, permitiendo avances significativos en este campo.
Citas
Agrawal, A. y Choudhary, A. (2019). Deep materials informatics: Applications of deep learning in materials science. MRS Communications, 9(3), 779-792. https://doi.org/10.1557/mrc.2019.73
Bagherzadeh, F. y Shafighfard, T. (2022). Ensemble machine learning approach for evaluating the material characterization of carbon nanotube-reinforced cementitious composites. Case Studies in Construction Materials, 17. https://doi.org/10.1016/j.cscm.2022.e01537
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. y Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547-555. https://doi.org/10.1038/s41586-018-0337-2
Čanađija, M. (2021). Deep learning framework for carbon nanotubes: Mechanical properties and modeling strategies. Carbon, 184, 891-901. https://doi.org/10.1016/j.carbon.2021.08.091
Cheng, Y., Wang, T. y Gang, Z. (2021). Artificial intelligence for materials science. Springer Series in Materials Science, 1. https://doi.org/https://doi.org/10.1007/978-3-030-68310-8
Förster, G. D., Castan, A., Loiseau, A., Nelayah, J., Alloyeau, D., Fossard, F., Bichara, C. y Amara, H. (2020). A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images. Carbon, 169, 465-474. https://doi.org/10.1016/j.carbon.2020.06.086
Isayev, O., Tropsha, A. y Curtarolo, S. (Eds.) (2019). Materials informatics: Methods, tools and applications. Wiley-VCH. https://doi.org/10.1002/9783527802265
Kajendirarajah, U., Olivia Avilés, M. y Lagugné-Labarthet, F. (2020). Deciphering tip-enhanced Raman imaging of carbon nanotubes with deep learning neural networks. Physical Chemistry Chemical Physics, 22(32), 17857–17866. https://doi.org/10.1039/D0CP02950E
Li, Y., Li, H., Jin, C. y Shen, J. (2022). The study of effect of carbon nanotubes on the compressive strength of cement-based materials based on machine learning. Construction and Building Materials, 358. https://doi.org/10.1016/j.conbuildmat.2022.129435
Liu, B., Vu-Bac, N., Zhuang, X., Fu, X. y Rabczuk, T. (2022). Stochastic full-range multiscale modeling of thermal conductivity of Polymeric carbon nanotubes composites: A machine learning approach. Composite Structures, 289. https://doi.org/10.1016/j.compstruct.2022.115393
Matos, M. A. S., Pinho, S. T. y Tagarielli, V. L. (2019). Predictions of the electrical conductivity of composites of polymers and carbon nanotubes by an artificial neural network. Scripta Materialia, 166, 117-121. https://doi.org/10.1016/j.scriptamat.2019.03.003
Morgan, D. y Jacobs, R. (2020). Opportunities and challenges for machine learning in materials science. Annual Review of Materials Research, 50, 71-103. https://doi.org/10.1146/annurev-matsci-070218-010015
Ni, D., Wu, W., Guo, Y., Gong, S. y Wang, Q. (2021). Identifying key parameters for predicting materials with low defect generation efficiency by machine learning. Computational Materials Science, 191. https://doi.org/10.1016/j.commatsci.2021.110306
Oliynyk, A. O. y Buriak, J. M. (2019). Virtual issue on machine-learning discoveries in materials science. Chemistry of Materials, 31(20), 8243-8247. https://doi.org/10.1021/acs.chemmater.9b03854
Rajan, K. (2013). Materials informatics: An introduction. En Informatics for Materials Science and Engineering: Data-Driven Discovery for Accelerated Experimentation and Application (pp. 1-16). Elsevier. https://doi.org/10.1016/B978-0-12-394399-6.00001-1
Ramezanizadeh, M., Ahmadi, M. H., Nazari, M. A., Sadeghzadeh, M. y Chen, L. (2019). A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids. Renewable and Sustainable Energy Reviews, 114. https://doi.org/10.1016/j.rser.2019.109345
Rickman, J. M., Lookman, T. y Kalinin, S. V. (2019). Materials informatics: From the atomic-level to the continuum. Acta Materialia, 168, 473-510. https://doi.org/10.1016/j.actamat.2019.01.051
Scarisoreanu, M., Ilie, A., Dutu, E., Badoi, A., Dumitrache, F., Tanasa, E., Mihailescu, C. N. y Mihailescu, I. (2019). Direct nanocrystallite size investigation in microstrained mixed phase TiO 2 nanoparticles by PCA of Raman spectra. Applied Surface Science, 470, 507-519. https://doi.org/10.1016/j.apsusc.2018.11.122
Sheremetyeva, N., Lamparski, M., Daniels, C., Van Troeye, B. y Meunier, V. (2020). Machine-learning models for Raman spectra analysis of twisted bilayer graphene. Carbon, 169, 455-464. https://doi.org/10.1016/j.carbon.2020.06.077
Singh, S., Junaid, Z. Bin, Vyas, V., Kalyanwat, T. S. y Rana, S. S. (2021). Identification of vacancy defects in carbon nanotubes using vibration analysis and machine learning. Carbon Trends, 5. https://doi.org/10.1016/j.cartre.2021.100091
Vasudevan, R., Pilania, G. y Balachandran, P. V. (2021). Machine learning for materials design and discovery. Journal of Applied Physics, 129(7). https://doi.org/10.1063/5.0043300
Vivanco, L. E., Martínez, C. L., Mercado, C. y Torres, C. (2022). Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review. Computational Materials Science, 201. https://doi.org/10.1016/j.commatsci.2021.110939
Wahab, H., Jain, V., Tyrrell, A. S., Seas, M. A., Kotthoff, L. y Johnson, P. A. (2020). Machine-learning-assisted fabrication: Bayesian optimization of laser-induced graphene patterning using in-situ Raman analysis. Carbon, 167, 609-619. https://doi.org/10.1016/j.carbon.2020.05.087
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.