Convergencia de la inteligencia artificial y la nanotecnología

  • Cesar Alberto Torres-Solis Universidad Nacional Autónoma de México, Centro de Física Aplicada y Tecnología Avanzada, Juriquilla, Qro. https://orcid.org/0000-0002-1283-2598
  • Mario Alan Quiroz-Juárez Universidad Nacional Autónoma de México, Centro de Física Aplicada y Tecnología Avanzada, Juriquilla, Qro. https://orcid.org/0000-0002-5995-9510
Palabras clave: inteligencia artificial, nanotecnología, redes neuronales, nanomateriales, nanofotónica

Resumen

La nanotecnología y la inteligencia artificial son dos campos científicos que individualmente han promovido una revolución científica y tecnológica en todo el mundo. Mientras la nanotecnología habilita la manipulación de materia a escalas nanométricas para desarrollar aplicaciones y tecnologías con propiedades únicas, la inteligencia artificial reúne un conjunto de técnicas efectivas para potencializar sistemas informáticos que desempeñen tareas de clasificación, optimización, predicción y reconocimiento de patrones, las cuales son típicamente atribuidas a los seres humanos. La intersección entre ambos campos constituye un área de investigación multidisciplinaria y moderna que promete impulsar una nueva generación de tecnologías y atender retos clave que contribuyan al avance de la ciencia. En este artículo se presenta una revisión general de los esfuerzos reportados en la literatura donde se explotan los atributos de autoaprendizaje de algunos algoritmos de inteligencia artificial en el contexto de nanotecnología. Adicionalmente, se discuten tendencias y perspectivas futuras donde convergen estos campos de investigación científica.

Citas

Ababneh, J. I. y Qasaimeh, O. (2006). Simple model for quantum-dot semiconductor optical amplifiers using artificial neural networks. IEEE Transactions on Electron Devices, 53(7): 1543-1550. https://doi.org/10.1109/TED.2006.875803.

Al-Khedher, M. A., Pezeshki, C., McHale, J. L. y Knorr, F. J. (2007). Quality classification via Raman identification and SEM analysis of carbon nanotube bundles using artificial neural networks. Nanotechnology, 18(35): 355703. https://doi.org/10.1088/0957-4484/18/35/355703.

Arlat, J., Kalbarczyk, Z. y Nanya, T. (2012). Nanocomputing: Small devices, large dependability challenges. IEEE Security & Privacy, 10(1): 69-72. https://doi.org/10.1109/MSP.2012.17.

Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... y Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58: 82-115. https://doi.org/10.1016/j.inffus.2019.12.012.

Asproulis, N. y Drikakis, D. (2009). Nanoscale materials modelling using neural networks. Journal of Computational and Theoretical Nanoscience, 6(3): 514-518. https://doi.org/10.1166/jctn.2009.1062.

Bishop, C. M. y Nasrabadi, N. M. (2006). Pattern recognition and machine learning, 4(4): 738. New York: springer.

Brunton, S. L. y Kutz, J. N. (2022). Data-driven science and engineering: Machine learning, dynamical systems and control. Cambridge University Press.

Brunton, S. L., Proctor, J. L. y Kutz, J. N. (2016). Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the national academy of sciences, 113(15): 3932-3937. https://doi.org/10.1073/pnas.1517384113.

Chen, S. H., Jakeman, A. J. y Norton, J. P. (2008). Artificial intelligence techniques: an introduction to their use for modelling environmental systems. Mathematics and computers in simulation, 78(2-3): 379-400. https://doi.org/10.1016/j.matcom.2008.01.028.

Feichtner, T., Selig, O., Kiunke, M. y Hecht, B. (2012). Evolutionary optimization of optical antennas. Physical review letters, 109(12): 127701. https://doi.org/10.1103/PhysRevLett.109.127701.

Forestiere, C., Donelli, M., Walsh, G. F., Zeni, E., Miano, G. y Dal Negro, L. (2010). Particle-swarm optimization of broadband nanoplasmonic arrays. Optics letters, 35(2): 133-135. https://doi.org/10.1364/OL.35.000133.

Fourches, D., Pu, D., Tassa, C., Weissleder, R., Shaw, S. Y., Mumper, R. J. y Tropsha, A. (2010). Quantitative nanostructure− activity relationship modeling. ACS nano, 4(10): 5703-5712. https://doi.org/10.1021/nn1013484.

Gadzhimagomedova, Z. M., Pashkov, D. M., Kirsanova, D. Y., Soldatov, S. A., Butakova, M. A., Chernov, A. V. y Soldatov, A. V. (2022). Artificial intelligence for nanostructured materials. Nanobiotechnology Reports, 17(1): 1-9. https://doi.org/10.1134/S2635167622010049.

Ginzburg, P., Berkovitch, N., Nevet, A., Shor, I. y Orenstein, M. (2011). Resonances on-demand for plasmonic nano-particles. Nano letters, 11(6): 2329-2333. https://doi.org/10.1021/nl200612f.

Goodfellow, I., Bengio, Y. y Courville, A. (2016). Deep learning. MIT press.

Ho, D., Wang, P., y Kee, T. (2019). Artificial intelligence in nanomedicine. Nanoscale Horizons, 4(2): 365-377. https://doi.org/10.1039/C8NH00233A.

Hulla, J. E., Sahu, S. C. y Hayes, A. W. (2015). Nanotechnology: history and future. Human & experimental toxicology, 34(12): 1318-1321. https://doi.org/ 10.1177/0960327115603588.

Jackson, P. C. (2019). Introduction to artificial intelligence, 3a ed. Nueva York: Courier Dover Publications.

Kim, C. E., Shin, H. S., Moon, P., Kim, H. J. y Yun, I. (2009). Modeling of In2O3-10 wt% ZnO thin film properties for transparent conductive oxide using neural networks. Current Applied Physics, 9(6): 1407-1410. https://doi.org/10.1016/j.cap.2009.03.013.

Krogh, A. (2008). What are artificial neural networks? Nature biotechnology, 26(2): 195-197. https://doi.org/10.1038/nbt1386.

Lalmuanawma, S., Hussain, J. y Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: a review. Chaos, Solitons & Fractals, 139: 110059. https://doi.org/10.1016/j.chaos.2020.110059.

Liu, D., Tan, Y., Khoram, E. y Yu, Z. (2018a). Training deep neural networks for the inverse design of nanophotonic structures. Acs Photonics, 5(4): 1365-1369. https://doi.org/10.1021/acsphotonics.7b01377.

Liu, Z., Zhu, D., Rodrigues, S. P., Lee, K. T. y Cai, W. (2018b). Generative model for the inverse design of metasurfaces. Nano letters, 18(10): 6570-6576. https://doi.org/10.1021/acs.nanolett.8b03171.

McCulloch, W. S. y Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4): 115-133. https://doi.org/10.1007/BF02478259.

Mitchell, T., Buchanan, B., DeJong, G., Dietterich, T., Rosenbloom, P. y Waibel, A. (1990). Machine learning. Annual review of computer science, 4(1): 417-433. https://doi.org/10.1146/annurev.cs.04.060190.002221.

Muliana, A., Haj-Ali, R. M., Steward, R. y Saxena, A. (2002). Artificial neural network and finite element modeling of nanoindentation tests. Metallurgical and Materials Transactions A, 33(7): 1939-1947. https://doi.org/10.1007/s11661-002-0027-3.

Murdoch, T. B. y Detsky, A. S. (2013). The inevitable application of big data to health care. Jama, 309(13): 1351-1352. https://doi.org/10.1001/jama.2013.393.

Nazari, A. y Azimzadegan, T. (2012). Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete. Materials Research, 15(3): 440-454. https://doi.org/10.1590/S1516-14392012005000057.

Nazari, A. y Riahi, S. (2010). Computer-aided prediction of physical and mechanical properties of high strength cementitious composite containing Cr2O3 nanoparticles. Nano, 5(05): 301-318. https://doi.org/10.1142/S1793292010002219.

Nielsen, M. A. (2015). Neural networks and deep learning, 25. San Francisco, CA, USA: Determination press.

Nikiforov, M. P., Reukov, V. V., Thompson, G. L., Vertegel, A. A., Guo, S., Kalinin, S. V. y Jesse, S. (2009). Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response. Nanotechnology, 20(40): 405708. https://doi.org/10.1088/0957-4484/20/40/405708.

Peurifoy, J., Shen, Y., Jing, L., Yang, Y., Cano-Renteria, F., DeLacy, B. G., ... y Soljačić, M. (2018). Nanophotonic particle simulation and inverse design using artificial neural networks. Science advances, 4(6): eaar4206. https://doi.org/10.1126/sciadv.aar4206.

Qu, Y., Jing, L., Shen, Y., Qiu, M. y Soljacic, M. (2019). Migrating knowledge between physical scenarios based on artificial neural networks. ACS Photonics, 6(5): 1168-1174. https://doi.org/10.1021/acsphotonics.8b01526.

Quiroz-Juárez, M. A., Torres-Gómez, A., Hoyo-Ulloa, I., León-Montiel, R. D. J. y U’Ren, A. B. (2021). Identification of high-risk COVID-19 patients using machine learning. PLoS One, 16(9): e0257234. https://doi.org/10.1371/journal.pone.0257234.

Rapaport, D. C. y Rapaport, D. C. R. (2004). The art of molecular dynamics simulation. Cambridge university press.

Razzak, M. I., Naz, S. y Zaib, A. (2018). Deep learning for medical image processing: overview, challenges and the future. Classification in BioApps, 323-350. https://doi.org/10.1007/978-3-319-65981-7_12.

Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6): 386. https://doi.org/10.1037/h0042519.

Sacha, G. M., Rodriguez, F. B. y Varona, P. (2009). An inverse problem solution for undetermined electrostatic force microscopy setups using neural networks. Nanotechnology, 20(8): 085702. https://doi.org/10.1088/0957-4484/20/8/085702.

Sacha, G. M. y Varona, P. (2013). Artificial intelligence in nanotechnology. Nanotechnology, 24(45): 452002. https://doi.org/10.1088/0957-4484/24/45/452002.

Shen, D., Wu, G. y Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19: 221. https://doi.org/10.1146/annurev-bioeng-071516-044442.

Singh, A. V., Ansari, M. H. D., Rosenkranz, D., Maharjan, R. S., Kriegel, F. L., Gandhi, K., ... y Luch, A. (2020). Artificial intelligence and machine learning in computational nanotoxicology: unlocking and empowering nanomedicine. Advanced Healthcare Materials, 9(17): 1901862. https://doi.org/10.1002/adhm.201901862.

So, S., Badloe, T., Noh, J., Bravo-Abad, J. y Rho, J. (2020). Deep learning enabled inverse design in nanophotonics. Nanophotonics, 9(5): 1041-1057. https://doi.org/10.1515/nanoph-2019-0474.

Uusitalo, M. A., Peltonen, J. y Ryhänen, T. (2011). Machine learning: how it can help nanocomputing. Journal of Computational and Theoretical Nanoscience, 8(8): 1347-1363. https://doi.org/10.1166/jctn.2011.1821.

Wetzstein, G., Ozcan, A., Gigan, S., Fan, S., Englund, D., Soljačić, M., ... y Psaltis, D. (2020). Inference in artificial intelligence with deep optics and photonics. Nature, 588(7836): 39-47. https://doi.org/10.1038/s41586-020-2973-6.

Wilson, B. y Km, G. (2020). Artificial intelligence and related technologies enabled nanomedicine for advanced cancer treatment. Nanomedicine, 15(05): 433-435. https://doi.org/10.2217/nnm-2019-0366.

Woolley, R. A., Stirling, J., Radocea, A., Krasnogor, N. y Moriarty, P. (2011). Automated probe microscopy via evolutionary optimization at the atomic scale. Applied Physics Letters, 98(25), 253104. https://doi.org/10.1063/1.3600662.

Xu, B., Shen, Z., Ni, X., Wang, J., Guan, J. y Lu, J. (2004). Determination of elastic properties of a film-substrate system by using the neural networks. Applied physics letters, 85(25): 6161-6163. https://doi.org/10.1063/1.1841472.

Yan, X., Sedykh, A., Wang, W., Yan, B. y Zhu, H. (2020). Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations. Nature communications, 11(1): 1-10. https://doi.org/10.1038/s41467-020-16413-3.

Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J. H. y Liao, Q. (2019). Deep learning for single image super-resolution: a brief review. IEEE Transactions on Multimedia, 21(12): 3106-3121. https://doi.org/10.1109/TMM.2019.2919431.

Publicado
2023-03-10
Cómo citar
Torres-Solis, C., & Quiroz-Juárez, M. (2023). Convergencia de la inteligencia artificial y la nanotecnología. Mundo Nano. Revista Interdisciplinaria En Nanociencias Y Nanotecnología, 16(31), 1e-14e. https://doi.org/10.22201/ceiich.24485691e.2023.31.69775