Convergencia de la inteligencia artificial y la nanotecnología

Contenido principal del artículo

Cesar Alberto Torres-Solis
https://orcid.org/0000-0002-1283-2598
Mario Alan Quiroz-Juárez
https://orcid.org/0000-0002-5995-9510

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.

Descargas

Los datos de descargas todavía no están disponibles.

Detalles del artículo

Cómo citar
Torres-Solis, C. A., & Quiroz-Juárez, M. A. (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
Sección
Artículos de revisión

Citas

Ababneh JI, Qasaimeh O. Simple model for quantum-dot semiconductor optical amplifiers using artificial neural networks. IEEE Transactions on Electron Devices. 2006;53(7):1543-50. DOI: https://doi.org/10.1109/TED.2006.875803

Al-Khedher MA, Pezeshki C, McHale JL, Knorr FJ. Quality classification via Raman identification and SEM analysis of carbon nanotube bundles using artificial neural networks. Nanotechnology. 2007;18(35):355703-. DOI: https://doi.org/10.1088/0957-4484/18/35/355703

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

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

Asproulis N, Drikakis D. Nanoscale materials modelling using neural networks. Journal of Computational and Theoretical Nanoscience. 2009;6(3):514-8. DOI: https://doi.org/10.1166/jctn.2009.1062

Bishop CM, Nasrabadi NM. Pattern recognition and machine learning. New York: springer; 2006.

Brunton SL, Kutz JN. Data-driven science and engineering: Machine learning, dynamical systems and control. Cambridge University Press; 2022. DOI: https://doi.org/10.1017/9781009089517

Brunton SL, Proctor JL, Kutz JN. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the national academy of sciences. 2016;113(15):3932-7. DOI: https://doi.org/10.1073/pnas.1517384113

Chen SH, Jakeman AJ, Norton JP. Artificial intelligence techniques: an introduction to their use for modelling environmental systems. Mathematics and computers in simulation. 2008;78(2-3):379-400. DOI: https://doi.org/10.1016/j.matcom.2008.01.028

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

Forestiere C, Donelli M, Walsh GF, Zeni E, Miano G, Dal Negro L. Particle-swarm optimization of broadband nanoplasmonic arrays. Optics letters. 2010;35(2):133-5. DOI: https://doi.org/10.1364/OL.35.000133

Fourches D, Pu D, Tassa C, Weissleder R, Shaw SY, Mumper RJ, Tropsha A. Quantitative nanostructure− activity relationship modeling. ACS nano. 2010;4(10):5703-12. DOI: https://doi.org/10.1021/nn1013484

Gadzhimagomedova ZM, Pashkov DM, Kirsanova DY, Soldatov SA, Butakova MA, Chernov AV, Soldatov AV. Artificial intelligence for nanostructured materials. Nanobiotechnology Reports. 2022;17(1):1-9. DOI: https://doi.org/10.1134/S2635167622010049

Ginzburg P, Berkovitch N, Nevet A, Shor I, Orenstein M. Resonances ondemand for plasmonic nano-particles. Nano letters. 2011;11(6):2329-33. DOI: https://doi.org/10.1021/nl200612f

Goodfellow I, Bengio Y, Courville A. Deep learning. MIT press; 2016.

Ho D, Wang P, Kee T. Artificial intelligence in nanomedicine. Nanoscale Horizons. 2019;4(2):365-77. DOI: https://doi.org/10.1039/C8NH00233A

Hulla JE, Sahu SC, Hayes AW. Nanotechnology: history and future. Human & experimental toxicology. 2015;34(12):1318-21. DOI: https://doi.org/10.1177/0960327115603588

Jackson PC. Introduction to artificial intelligence. Nueva York: Courier Dover Publications; 2019. DOI: https://doi.org/10.18356/d94175df-en

Kim CE, Shin HS, Moon P, Kim HJ, Yun I. Modeling of In2O3-10 wt% ZnO thin film properties for transparent conductive oxide using neural networks. Current Applied Physics. 2009;9(6):1407-10. DOI: https://doi.org/10.1016/j.cap.2009.03.013

Krogh A. What are artificial neural networks?. Nature biotechnology. 2008;26(2):195-7. DOI: https://doi.org/10.1038/nbt1386

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

Liu D, Tan Y, Khoram E, Yu Z. Training deep neural networks for the inverse design of nanophotonic structures. Acs Photonics. 2018;5(4):1365-9. DOI: https://doi.org/10.1021/acsphotonics.7b01377

Liu Z, Zhu D, Rodrigues SP, Lee KT, Cai W. Generative model for the inverse design of metasurfaces. Nano letters. 2018;18(10):6570-6. DOI: https://doi.org/10.1021/acs.nanolett.8b03171

McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics. 1943;5(4):115-33. DOI: https://doi.org/10.1007/BF02478259

Mitchell T, Buchanan B, DeJong G, Dietterich T, Rosenbloom P, Waibel A. Machine learning. Annual review of computer science. 1990;4(1):417-33. DOI: https://doi.org/10.1146/annurev.cs.04.060190.002221

Muliana A, Haj-Ali RM, Steward R, Saxena A. Artificial neural network and finite element modeling of nanoindentation tests. Metallurgical and Materials Transactions A. 2002;33(7):1939-47. DOI: https://doi.org/10.1007/s11661-002-0027-3

Murdoch TB, Detsky AS. The inevitable application of big data to health care. Jama. 2013;309(13):1351-2. DOI: https://doi.org/10.1001/jama.2013.393

Nazari A, Azimzadegan T. Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete. Materials Research. 2012;15(3):440-54. DOI: https://doi.org/10.1590/S1516-14392012005000057

Nazari A, Riahi S. Computer-aided prediction of physical and mechanical properties of high strength cementitious composite containing Cr2O3 nanoparticles. Nano. 2010;5(05):301-18. DOI: https://doi.org/10.1142/S1793292010002219

Nielsen MA. Neural networks and deep learning. San Francisco, CA, USA: Determination press; 2015.

Nikiforov MP, Reukov VV, Thompson GL, Vertegel AA, Guo S, Kalinin SV, Jesse S. Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response. Nanotechnology. 2009;20(40):405708-. DOI: https://doi.org/10.1088/0957-4484/20/40/405708

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

Qu Y, Jing L, Shen Y, Qiu M, Soljacic M. Migrating knowledge between physical scenarios based on artificial neural networks. ACS Photonics. 2019;6(5):1168-74. DOI: https://doi.org/10.1021/acsphotonics.8b01526

Quiroz-Juárez MA, Torres-Gómez A, Hoyo-Ulloa I, León-Montiel RDJ, U’Ren AB. Identification of high-risk COVID-19 patients using machine learning. PLoS One. 2021;16(9). DOI: https://doi.org/10.1371/journal.pone.0257234

Rapaport DC, Rapaport DCR. The art of molecular dynamics simulation. Cambridge university press; 2004. DOI: https://doi.org/10.1017/CBO9780511816581

Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: overview, challenges and the future. Classification in BioApps. 2018;:323-50. DOI: https://doi.org/10.1007/978-3-319-65981-7_12

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

Sacha GM, Rodriguez FB, Varona P. An inverse problem solution for undetermined electrostatic force microscopy setups using neural networks. Nanotechnology. 2009;20(8):085702-. DOI: https://doi.org/10.1088/0957-4484/20/8/085702

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

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

Singh AV, Ansari MHD, Rosenkranz D, Maharjan RS, Kriegel FL, Gandhi K, Luch A. Artificial intelligence and machine learning in computational nanotoxicology: unlocking and empowering nanomedicine. Advanced Healthcare Materials. 2020;9(17):1901862-. DOI: https://doi.org/10.1002/adhm.201901862

So S, Badloe T, Noh J, Bravo-Abad J, Rho J. Deep learning enabled inverse design in nanophotonics. Nanophotonics. 2020;9(5):1041-57. DOI: https://doi.org/10.1515/nanoph-2019-0474

Uusitalo MA, Peltonen J, Ryhänen T. Machine learning: how it can help nanocomputing. Journal of Computational and Theoretical Nanoscience. 2011;8(8):1347-63. DOI: https://doi.org/10.1166/jctn.2011.1821

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

Wilson B, Km G. Artificial intelligence and related technologies enabled nanomedicine for advanced cancer treatment. Nanomedicine. 2020;15(05):433-5. DOI: https://doi.org/10.2217/nnm-2019-0366

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

Xu B, Shen Z, Ni X, Wang J, Guan J, Lu J. Determination of elastic properties of a film-substrate system by using the neural networks. Applied physics letters. 2004;85(25):6161-3. DOI: https://doi.org/10.1063/1.1841472

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

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