Generic placeholder image

Recent Innovations in Chemical Engineering

Editor-in-Chief

ISSN (Print): 2405-5204
ISSN (Online): 2405-5212

Mini-Review Article

The Convergence of Nanotechnology and Artificial Intelligence: Unlocking Future Innovations

Author(s): Sarvat Zafar* and Nadim Rana

Volume 18, Issue 2, 2025

Published on: 04 February, 2025

Page: [85 - 99] Pages: 15

DOI: 10.2174/0124055204359215250127071256

Price: $65

Abstract

This review article explores the integration of artificial intelligence (AI) and nanotechnology, focusing on their combined potential to drive advancements in nanomaterial discovery, drug delivery systems, and nano-electronic component design. It also examines the transformative effects of AI-enhanced nanotechnology in medicine, diagnostics, bioengineering, and other scientific domains, emphasizing its future implications across various sectors. This article examines the synergy between AI and nanotechnology, focusing on recent innovations in nanomaterial discovery, AI-driven material design, and precision medicine. It reviews case studies and research highlighting AI's role in accelerating nanomaterial development and its applications in medicine, electronics, diagnostics, and robotics, using a multidisciplinary approach. AI-enhanced nanotechnology has enabled the development of novel nanomaterials with unprecedented properties tailored for specific applications, such as highly efficient drug delivery systems and next-generation nanoelectronic components. In medicine, AI-driven nanotechnology offers promising solutions for highly personalized treatments, improving therapeutic efficacy and reducing side effects. Additionally, AI is driving innovation in diagnostics and robotics, leading to more sensitive diagnostic tools and the development of nanoscale-precision robotic systems. The integration of AI and nanotechnology presents vast opportunities for scientific and technological advancements. As AI algorithms continue to evolve, their impact on nanotechnology will lead to breakthroughs in diverse fields, such as medicine, electronics, diagnostics, and robotics. This interdisciplinary synergy will open new frontiers in research, driving transformative changes in bioengineering, neuroscience, and beyond.

Keywords: Artificial intelligence, nanotechnology, nanomaterial discovery, precision medicine, nanodevice design, AI-enhanced diagnostics.

« Previous
Graphical Abstract
[1]
Nasrollahzadeh M, Sajadi SM, Sajjadi M, Issaabadi Z. An introduction to nanotechnology Interface science and technology 28. New York: Elsevier 2019; pp. 1-27.
[http://dx.doi.org/10.1016/B978-0-12-813586-0.00001-8]
[2]
Sacha GM, Varona P. Artificial intelligence in nanotechnology. Nanotechnology 2013; 24(45): 452002.
[http://dx.doi.org/10.1088/0957-4484/24/45/452002] [PMID: 24121558]
[3]
Agboklu M. The impact of artificial intelligence on innovative nanotechnologies for advanced medical diagnosis. J Nanotech Res 2024; 6(1): 1-5.
[http://dx.doi.org/10.26502/jnr.2688-85210040]
[4]
Haick H, Tang N. Artificial intelligence in medical sensors for clinical decisions. ACS Nano 2021; 15(3): 3557-67.
[http://dx.doi.org/10.1021/acsnano.1c00085] [PMID: 33620208]
[5]
Kalinin SV, Ziatdinov M, Hinkle J, et al. Automated and autonomous experiments in electron and scanning probe microscopy. ACS Nano 2021; 15(8): 12604-27.
[http://dx.doi.org/10.1021/acsnano.1c02104] [PMID: 34269558]
[6]
Azuri I, Rosenhek-Goldian I, Regev-Rudzki N, Fantner G, Cohen SR. The role of convolutional neural networks in scanning probe microscopy: A review. Beilstein J Nanotechnol 2021; 12(1): 878-901.
[http://dx.doi.org/10.3762/bjnano.12.66] [PMID: 34476169]
[7]
Chugh V, Basu A, Kaushik A, Basu AK. Progression in quantum sensing/bio-sensing technologies for healthcare. ECS Sens Plus 2023; 2(1): 015001.
[http://dx.doi.org/10.1149/2754-2726/acc190]
[8]
Shwetha K, Amogh P, Vaidya SS, Hariprasad N, Krishna S, Manjunatha C. Nanorobotics for Advancing biomedicine: Progresses in Materials, Design, Fabrication, Opportunities and Applications: IEEE Access 2024; 10: 10562253.
[http://dx.doi.org/10.1109/ACCESS.2024.10562253]
[9]
Vasoya N. Revolutionizing nano materials processing through IoT-AI integration: Opportunities and challenges. J Mater Sci Res Rev 2023; 6(3): 294-328.
[10]
Krull A, Hirsch P, Rother C, Schiffrin A, Krull C. Artificial-intelligence-driven scanning probe microscopy. Commun Phys 2020; 3(1): 54.
[http://dx.doi.org/10.1038/s42005-020-0317-3]
[11]
Rahman Laskar MA, Celano U. Scanning probe microscopy in the age of machine learning APL Mach Learn 2023; 1(4)
[http://dx.doi.org/10.1063/5.0160568]
[12]
Vijayaraghavan V, Garg A, Wong CH, et al. A molecular dynamics based artificial intelligence approach for characterizing thermal transport in nanoscale material. Thermochim Acta 2014; 594: 39-49.
[http://dx.doi.org/10.1016/j.tca.2014.08.029]
[13]
Athanasopoulou K, Daneva GN, Adamopoulos PG, Scorilas A. Artificial intelligence: The milestone in modern biomedical research. BioMedInformatics 2022; 2(4): 727-44.
[http://dx.doi.org/10.3390/biomedinformatics2040049]
[14]
Diaz-Flores E, Meyer T, Giorkallos A. Evolution of artificial intelligence-powered technologies in biomedical research and healthcare. Smart Biol Futur 2022; 182: 23-60.
[http://dx.doi.org/10.1007/10_2021_189]
[15]
Molas G, Nowak E. Advances in emerging memory technologies: From data storage to artificial intelligence. Appl Sci 2021; 11(23): 11254.
[http://dx.doi.org/10.3390/app112311254]
[16]
Liu C, Chen H, Wang S, et al. Two-dimensional materials for next-generation computing technologies. Nat Nanotechnol 2020; 15(7): 545-57.
[http://dx.doi.org/10.1038/s41565-020-0724-3] [PMID: 32647168]
[17]
Peng J, Muhammad R, Wang SL, Zhong HZ. How machine learning accelerates the development of quantum dots? Chin J Chem 2021; 39(1): 181-8.
[http://dx.doi.org/10.1002/cjoc.202000393]
[18]
Ali MK, Javaid S, Afzal H, et al. Exploring the multifunctional roles of quantum dots for unlocking the future of biology and medicine. Environ Res 2023; 232: 116290.
[http://dx.doi.org/10.1016/j.envres.2023.116290] [PMID: 37295589]
[19]
Hamedi S, Kordrostami Z, Yadollahi A. Artificial neural network approaches for modeling absorption spectrum of nanowire solar cells. Neural Comput Appl 2019; 31(12): 8985-95.
[http://dx.doi.org/10.1007/s00521-019-04406-3]
[20]
Zhang Z, Liu X, Zhou H, Xu S, Lee C. Advances in machine‐learning enhanced nanosensors: From cloud artificial intelligence toward future edge computing at chip level. Small Struct 2024; 5(4): 2300325.
[http://dx.doi.org/10.1002/sstr.202300325]
[21]
Hu J, Wang W, Yu H. Endowing soft photo‐actuators with intelligence. Adv Intell Syst 2019; 1(8): 1900050.
[http://dx.doi.org/10.1002/aisy.201900050]
[22]
Javaid M, Haleem A, Singh RP, Rab S, Suman R. Exploring the potential of nanosensors: A brief overview. Sens Inter 2021; 2: 100130.
[http://dx.doi.org/10.1016/j.sintl.2021.100130]
[23]
Zeng H, Wasylczyk P, Wiersma DS, Priimagi A. Light robots: Bridging the gap between microrobotics and photomechanics in soft materials. Adv Mater 2018; 30(24): 1703554.
[http://dx.doi.org/10.1002/adma.201703554] [PMID: 29067734]
[24]
Chen H, Zheng Y, Li J, Li L, Wang X. AI for nanomaterials development in clean energy and carbon capture, utilization and storage (CCUS). ACS Nano 2023; 17(11): 9763-92.
[http://dx.doi.org/10.1021/acsnano.3c01062] [PMID: 37267448]
[25]
Tabor DP, Roch LM, Saikin SK, et al. Accelerating the discovery of materials for clean energy in the era of smart automation. Nat Rev Mater 2018; 3(5): 5-20.
[http://dx.doi.org/10.1038/s41578-018-0005-z]
[26]
Jia Y, Hou X, Wang Z, Hu X. Machine learning boosts the design and discovery of nanomaterials. ACS Sustain Chem& Eng 2021; 9(18): 6130-47.
[http://dx.doi.org/10.1021/acssuschemeng.1c00483]
[27]
Hong Y, Hou B, Jiang H, Zhang J. Machine learning and artificial neural network accelerated computational discoveries in materials science. Wiley Interdiscip Rev Comput Mol Sci 2020; 10(3): e1450.
[http://dx.doi.org/10.1002/wcms.1450]
[28]
Afroze S, Reza MS, Amin MR, Taweekun J, Azad AK. Progress in nanomaterials fabrication and their prospects in artificial intelligence towards solid oxide fuel cells: A review. Int J Hydr Ener 2024; 52: 216-47.
[http://dx.doi.org/10.1016/j.ijhydene.2022.11.335]
[29]
Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature 2018; 559(7715): 547-55.
[http://dx.doi.org/10.1038/s41586-018-0337-2] [PMID: 30046072]
[30]
Zhu X, Li Y, Gu N. Application of artificial intelligence in the exploration and optimization of biomedical nanomaterials. Nano Biomed Eng 2023; 15(3): 342-53.
[http://dx.doi.org/10.26599/NBE.2023.9290035]
[31]
Kaur S, Singla J, Nkenyereye L, et al. Medical diagnostic systems using artificial intelligence (AI) algorithms: Principles and perspectives. IEEE Access 2020; 8: 228049-69.
[http://dx.doi.org/10.1109/ACCESS.2020.3042273]
[32]
Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev 2019; 151-152: 169-90.
[http://dx.doi.org/10.1016/j.addr.2019.05.001] [PMID: 31071378]
[33]
Alshawwa SZ, Kassem AA, Farid RM, Mostafa SK, Labib GS. Nanocarrier drug delivery systems: Characterization, limitations, future perspectives and implementation of artificial intelligence. Pharmaceutics 2022; 14(4): 883.
[http://dx.doi.org/10.3390/pharmaceutics14040883] [PMID: 35456717]
[34]
Vetrova NA, Filyaev AA. Evaluation criterion of the neural network model of heterostructural nanoelectronic devices for predicting their electrical parameters. RUDN J Eng Res 2022; 23(1): 7-14.
[35]
Košmerl V, Štajduhar I, Čanađija M. Predicting stress–strain behavior of carbon nanotubes using neural networks. Neural Comput Appl 2022; 34(20): 17821-36.
[http://dx.doi.org/10.1007/s00521-022-07430-y]
[36]
Vijayaraghavan V, Garg A, Wong CH, Tai K. Estimation of mechanical properties of nanomaterials using artificial intelligence methods. Appl Phys, A Mater Sci Process 2014; 116(3): 1099-107.
[http://dx.doi.org/10.1007/s00339-013-8192-3]
[37]
Huang G, Guo Y, Chen Y, Nie Z. Application of machine learning in material synthesis and property prediction. Materials 2023; 16(17): 5977.
[http://dx.doi.org/10.3390/ma16175977] [PMID: 37687675]
[38]
Raju RK. Exploring nanocluster potential energy surfaces via deep reinforcement learning: Strategies for global minimum search. J Phys Chem A 2024; 128(42): 9122-34.
[http://dx.doi.org/10.1021/acs.jpca.4c04416] [PMID: 39397328]
[39]
Vivanco-Benavides LE, Martínez-González CL, Mercado-Zúñiga C, Torres-Torres C. Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review. Comput Mater Sci 2022; 201: 110939.
[http://dx.doi.org/10.1016/j.commatsci.2021.110939]
[40]
Lan T, Wang H, An Q. Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms. Nat Commun 2024; 15(1): 6281.
[http://dx.doi.org/10.1038/s41467-024-50531-6] [PMID: 39060277]
[41]
Zhou Z, Li X, Zare RN. Optimizing chemical reactions with deep reinforcement learning. ACS Cent Sci 2017; 3(12): 1337-44.
[http://dx.doi.org/10.1021/acscentsci.7b00492] [PMID: 29296675]
[42]
Brown KA, Brittman S, Maccaferri N, Jariwala D, Celano U. Machine learning in nanoscience: Big data at small scales. Nano Lett 2020; 20(1): 2-10.
[http://dx.doi.org/10.1021/acs.nanolett.9b04090] [PMID: 31804080]
[43]
Badini S, Regondi S, Pugliese R. Unleashing the power of artificial intelligence in materials design. Materials 2023; 16(17): 5927.
[http://dx.doi.org/10.3390/ma16175927] [PMID: 37687620]
[44]
Pyzer-Knapp EO, Pitera JW, Staar PW, et al. Accelerating materials discovery using artificial intelligence, high performance computing and robotics. NPJ Comput Mater 2022; 8: 1-84.
[45]
Wang K, Dowling AW. Bayesian optimization for chemical products and functional materials. Curr Opin Chem Eng 2022; 36: 100728.
[http://dx.doi.org/10.1016/j.coche.2021.100728]
[46]
Shi J, Kantoff PW, Wooster R, Farokhzad OC. Cancer nanomedicine: Progress, challenges and opportunities. Nat Rev Cancer 2017; 17(1): 20-37.
[http://dx.doi.org/10.1038/nrc.2016.108] [PMID: 27834398]
[47]
Manoharan H, Teekaraman Y, Kuppusamy R, Radhakrishnan A, Venkatachalam HK. Acclimatization of nanorobots in medical applications using the artificial intelligence system with the data transfer approach. Wirel Commun Mob Comput 2022; 2022(1): 1-8.
[http://dx.doi.org/10.1155/2022/5877042]
[48]
Izanker SV, Dhole A, Kumar P. Navigating the nexus: Exploring the fusion of AI and nanotechnology for cutting-edge advances. In: Proceedings of the 2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0 (IDICAIEI); 2023 Nov 27–28; Wardha, India.
[http://dx.doi.org/10.1109/IDICAIEI58380.2023.10406387]
[49]
Nandipati M, Fatoki O, Desai S. Bridging nanomanufacturing and artificial intelligence—a comprehensive review. Materials 2024; 17(7): 1621.
[http://dx.doi.org/10.3390/ma17071621] [PMID: 38612135]
[50]
Man F, Lammers TTM, de Rosales R. Imaging nanomedicine-based drug delivery: A review of clinical studies. Mol Imaging Biol 2018; 20(5): 683-95.
[http://dx.doi.org/10.1007/s11307-018-1255-2] [PMID: 30084044]
[51]
Sharma V, Singh A, Chauhan S, et al. Role of artificial intelligence in drug discovery and target identification in cancer. Curr Drug Deliv 2024; 21(6): 870-86.
[http://dx.doi.org/10.2174/1567201821666230905090621] [PMID: 37670704]
[52]
Blanco-González A, Cabezón A, Seco-González A, et al. The role of AI in drug discovery: Challenges, opportunities, and strategies. Pharmaceuticals 2023; 16(6): 891.
[http://dx.doi.org/10.3390/ph16060891] [PMID: 37375838]
[53]
Pawar V, Patil A, Tamboli F, Gaikwad D, Mali D, Shinde A. Harnessing the power of AI in pharmacokinetics and pharmacodynamics: A comprehensive review. AAPS PharmSciTech 2021; 14(2): 426-39.
[54]
Tiwari PC, Pal R, Chaudhary MJ, Nath R. Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges. Drug Dev Res 2023; 84(8): 1652-63.
[http://dx.doi.org/10.1002/ddr.22115] [PMID: 37712494]
[55]
Massaro A. Intelligent materials and nanomaterials improving physical properties and control oriented on electronic implementations. Electronics 2023; 12(18): 3772.
[http://dx.doi.org/10.3390/electronics12183772]
[56]
Pomerantseva E, Bonaccorso F, Feng X, Cui Y, Gogotsi Y. Energy storage: The future enabled by nanomaterials. Science 2019; 366(6468): eaan8285.
[http://dx.doi.org/10.1126/science.aan8285] [PMID: 31753970]
[57]
Chen YP, Bashir S, Liu JL, Eds. Nanostructured materials for next-generation energy storage and conversion. Cham: Springer 2017; p. 546.
[http://dx.doi.org/10.1007/978-3-662-53514-1]
[58]
Zhang Q, Uchaker E, Candelaria SL, Cao G. Nanomaterials for energy conversion and storage. Chem Soc Rev 2013; 42(7): 3127-71.
[http://dx.doi.org/10.1039/c3cs00009e] [PMID: 23455759]
[59]
Yip M, Salcudean S, Goldberg K, et al. Artificial intelligence meets medical robotics. Science 2023; 381(6654): 141-6.
[http://dx.doi.org/10.1126/science.adj3312] [PMID: 37440630]
[60]
Adir O, Poley M, Chen G, et al. Integrating artificial intelligence and nanotechnology for precision cancer medicine. Adv Mater 2020; 32(13): 1901989.
[http://dx.doi.org/10.1002/adma.201901989] [PMID: 31286573]
[61]
Hayat H, Nukala A, Nyamira A, Fan J, Wang P. A concise review: The synergy between artificial intelligence and biomedical nanomaterials that empowers nanomedicine. Biomed Mater 2021; 16(5): 052001.
[http://dx.doi.org/10.1088/1748-605X/ac15b2] [PMID: 34280907]
[62]
Ahmad S, Khan FN, Ramlal A, Begum S, Qazi S, Raza K. Nanoinformatics and nanomodeling: Recent developments in computational nanodrug design and delivery systems. Emerg Nanotechnol Med Appl 2023; pp. 297-332.
[63]
Hassabis D, Kumaran D, Summerfield C, Botvinick M. Neuroscience-inspired artificial intelligence. Neuron 2017; 95(2): 245-58.
[http://dx.doi.org/10.1016/j.neuron.2017.06.011] [PMID: 28728020]
[64]
Dzobo K, Adotey S, Thomford NE, Dzobo W. Integrating artificial and human intelligence: A partnership for responsible innovation in biomedical engineering and medicine. OMICS 2020; 24(5): 247-63.
[http://dx.doi.org/10.1089/omi.2019.0038] [PMID: 31313972]
[65]
Winkler DA. Role of artificial intelligence and machine learning in nanosafety. Small 2020; 16(36): 2001883.
[http://dx.doi.org/10.1002/smll.202001883] [PMID: 32537842]
[66]
Sriram T, Chakraborty T, Prasanna PM. Artificial intelligence powered insights into nanotoxicology. Inter J Adv Life Sci Res 2024; 7(2): 68-80.
[http://dx.doi.org/10.31632/ijalsr.2024.v07i02.005]
[67]
Singh AV, Rosenkranz D, Ansari MHD, et al. Artificial intelligence and machine learning empower advanced biomedical material design to toxicity prediction. Adv Intell Syst 2020; 2(12): 2000084.
[http://dx.doi.org/10.1002/aisy.202000084]
[68]
Zafar S, Rana N. Artificial intelligence in material science.In: Artificial intelligence and data science for advanced materials. CRC Press 2023; pp. 1-18.
[http://dx.doi.org/10.1201/9781003437369-1]
[69]
Rana N, Latiff MSA, Abdulhamid SM, Chiroma H. Whale optimization algorithm: A systematic review of contemporary applications, modifications and developments. Neural Comput Appl 2020; 32(20): 16245-77.
[http://dx.doi.org/10.1007/s00521-020-04849-z]
[70]
Chiroma H, Abdullahi UA, Abdulhamid SM, et al. Progress on artificial neural networks for big data analytics: A survey. IEEE Access 2019; 7: 70535-51.
[http://dx.doi.org/10.1109/ACCESS.2018.2880694]
[71]
Malik S, Muhammad K, Waheed Y. Nanotechnology: A revolution in modern industry. Molecules 2023; 28(2): 661.
[http://dx.doi.org/10.3390/molecules28020661] [PMID: 36677717]
[72]
Hulsen T. Literature analysis of artificial intelligence in biomedicine. Ann Transl Med 2022; 10(23): 1284.
[http://dx.doi.org/10.21037/atm-2022-50] [PMID: 36618779]
[73]
Shastri BJ, Tait AN, Ferreira de Lima T, et al. Photonics for artificial intelligence and neuromorphic computing. Nat Photonics 2021; 15(2): 102-14.
[http://dx.doi.org/10.1038/s41566-020-00754-y]
[74]
Jakšić Z, Devi S, Jakšić O, Guha K. A comprehensive review of bio-inspired optimization algorithms including applications in microelectronics and nanophotonics. Biomimetics 2023; 8(3): 278.
[http://dx.doi.org/10.3390/biomimetics8030278] [PMID: 37504166]
[75]
Botifoll M, Pinto-Huguet I, Arbiol J. Machine learning in electron microscopy for advanced nanocharacterization: Current developments, available tools and future outlook. Nanoscale Horiz 2022; 7(12): 1427-77.
[http://dx.doi.org/10.1039/D2NH00377E] [PMID: 36239693]
[76]
Naik GG, Jagtap VA. Two heads are better than One: unravelling the potential impact of artificial intelligence in nanotechnology. Nano TransMed 2024; p. 100041.
[77]
Regli W, Rossignac J, Shapiro V, Srinivasan V. The new frontiers in computational modeling of material structures. Comput Aided Des 2016; 77: 73-85.
[http://dx.doi.org/10.1016/j.cad.2016.03.002]
[78]
Malaca P, Rocha LF, Gomes D, Silva J, Veiga G. Online inspection system based on machine learning techniques: Real case study of fabric textures classification for the automotive industry. J Intell Manuf 2019; 30(1): 351-61.
[http://dx.doi.org/10.1007/s10845-016-1254-6]

Rights & Permissions Print Cite
© 2025 Bentham Science Publishers | Privacy Policy