Keywords
Nanomaterials, Machine learning, Nanocomposites, Artificial Neural Networks, Molecular Design, artificial intelligence, Nanotechnology, AI
Document Type
Review Paper
Abstract
Artificial intelligence (AI) is emerging as a prominent technological advancement. It is the act of replicating human intelligence for many purposes. In contrast to conventional methodologies, artificial intelligence (AI) is undergoing tremendous advancements. The present state of artificial intelligence (AI) technology enables them to effectively address numerous intricate difficulties with proficiency comparable to a human's. The significance of advancements in AI is particularly evident in machine learning, where the techniques and algorithms are effectively applied to address many problems, including those in nanotechnology. In contemporary nanotechnology, it is crucial to expedite the search for the most favorable synthesis parameters while developing novel nanomaterials. The convergence of machine learning and nanotechnology necessitates a comprehensive examination of existing data on the application of artificial intelligence (AI) in addressing challenges in the nanomaterials science field. This review should encompass various stages, including computer design, chemical synthesis, and diagnostics of the resultant nanomaterials. Significant emphasis is placed on employing machine-learning technologies to investigate the thermal and dynamic characteristics of nanofluids, the sorption processes of nanocomposites, the catalytic activity of nanoparticles, and the toxicity of nanoparticles. Additionally, these technologies are utilized to address nanosensor issues and process experimental data acquired during the diagnostics of different nanomaterial properties.
References
L. Dung, L. Paramonov, C. Davidson, J. Ramsden, H. Wright, N. Holliman, J. Hagon, M. Heggie, C. Makatsoris, The Matter Compiler-towards atomically precise engineering and manufacture, Nanotech. Perce., 7 (2011) 199-217. https://doi.org/10.56801/nano-ntp.v7i3.68 Mitchell, T. M. Machine learning; MA: McGraw-Hill, Boston, 1997. Bishop,C.M., Nasrabadi, N.M. Pattern Recognition and Machine Learning; Springer, New York, NY, USA, 2006. T. F. Cova, and A. A. Pais, Deep learning for deep chemistry: optimizing the prediction of chemical patterns. Front. Chem., 7 (2019) 809. https://doi.org/10.3389/fchem.2019.00809 H. Shindo, Y. Matsumoto, Gated Graph Recursive Neural Networks for Molecular Property Prediction, https://doi.org/10.48550/arXiv.1909.00259 Reisfeld, B. Mayeno, A.N. What is Computational Toxicology?; Humana Press: Springer Protocols, NJ, USA, 2012. G. Chen, M.G. Vijver, Y. Xiao, W. J. G.M., Peijnenburg, A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials. Materials, 10 (2017) 1013. https://doi.org/10.3390/ma10091013 B. Saini, S. Srivastava, Nanotoxicity prediction using computational modelling - review and future directions, IOP Conf. Ser. Mater. Sci. Eng., 348 (2018) 012005. https://doi.org/10.1088/1757-899X/348/1/012005 C. Zhao, E. Boriani, A. Chana, A. Roncaglioni, E. Benfenati, A new hybrid system of QSAR models for predicting bioconcentration factors (BCF), Chemosphere, 73 (2008) 1701. https://doi.org/10.1016/j.chemosphere.2008.09.033 Leszczynski, J., Kaczmarek-Kedziera, A., Puzyn, T., Papadopoulos, M. G., Reis, H., Shukla, M. K. Handbook of Computational Chemistry; Springer Cham: Springer International Publishing, Switzerland, 2017. H. Kubinyi, From Narcosis to Hyperspace: The History of QSAR, Quant. Struct.-Act. Relat, 21 (2002) 348. A. Singh, M. Ansari, D. Rosenkranz, R. Maharjan, F. Kriegel, K. Gandhi, A. Kanase, R. Singh, P. Laux, A. Luch, Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine, Adv. Healthc. Mater., 9 (2020) 170. https://doi.org/10.1002/adhm.201901862 K. Pearson, LIII. On lines and planes of closest fit to systems of points in space, London Edinburgh Dublin Philos. Mag. J. Sci., 2 (1901) 559-572. https://doi.org/10.1080/14786440109462720 A. Ben-Hur, D. Horn, H.T. Siegelmann, Support Vector Clustering, J. Mach. Learn. Res., 2 (2001) 125-137. T. K. Ho, Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, Canada, August 1995. https://doi.org/10.1109/ICDAR.1995.598929 S. Vanhuffel, J. Vandewalle, The partial total least squares algorithm J. Comput. Appl. Math., 21 (1988) 333-341. https://doi.org/10.1016/0377-0427(88)90317-2 T. Kohonen, Self-organized formation of topologically correct feature maps, Biol. Cybern., 43 (1982) 59–69. https://doi.org/10.1007/BF00337288 M. Sato, A real time learning algorithm for recurrent analog neural networks, Biol. Cybern., 62 (1990) 237–241. https://doi.org/10.1007/BF00198098 B.E. Boser, COLT '92: Proc. of the Fifth Annu.Workshop on Computational Learning Theory, Association for Computing Machinery: New York, 1992. C. Hansch, Quantitative approach to biochemical structure-activity relationships, Acc. Chem. Res.., 2 (1969) 232–239. https://doi.org/10.1021/ar50020a002 T. Teorell, Kinetics of distribution of substances administered to the body, II: The extravascular modes of administration, Arch. Int. Pharmacodyn. Ther., 57 (1937) 226-240. https://api.semanticscholar.org/CorpusID:53883583 Overtone, F. Applied Physiology: Including the Effects of Alcohol and Narcotics; American Book Company: New York, 1897. J.W. Wilson, S.M. Free, A mathematical contribution to structure-activity studies, J. Med. Chem., 7 (1964) 395–399. https://doi.org/10.1021/jm00334a001 C.A. Lipinski, F. Lombardo, B.W. Dominy, P.J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Delivery Rev., 23 (1997) 3-25. https://doi.org/10.1016/S0169-409X(96)00423-1 Hammett, L. P., Physical Organic Chemistry; McGraw-Hill: New York, 1940. M. Hosseini-Yeganeh, A.J. McLachlan, Antimicrob. Agents Chemother., 46 (2002) 2219-2228. https://doi.org/10.1128/AAC.46.7.2219-2228.2002 S. Persiani, M. D’Amato, A. Jakate, P. Roy, J. Wangsa, R. Kapil, L.C. Rovati, Pharmacokinetic Profile of Dexloxiglumide, Clin. Pharmacokinet., 45 (2006) 1177-1188. https://doi.org/10.2165/00003088-200645120-00003 R. Beaudouin, S. Micallef, C. Brochot, A stochastic whole-body physiologically based pharmacokinetic model to assess the impact of inter-individual variability on tissue dosimetry over the human lifespan, Regul. Toxicol. Pharmacol., 57 (2010) 103. https://doi.org/doi:10.1016/j.yrtph.2010.01.005 D.S. Li, M. Morishita, J.G. Wagner, M. Fatouraie, M. Wooldridge, W.E. Eagle, J. Barres, U. Carlander, C. Emond, O. Jolliet, In vivo biodistribution and physiologically based pharmacokinetic modeling of inhaled fresh and aged cerium oxide nanoparticles in rats, Part. Fibre Toxicol., 13 (2016) 45. https://doi.org/10.1186/s12989-016-0156-2 E. Price, A.J. Gesquiere, An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions, Sci. Rep., 9 (2019) 13943. https://doi.org/10.1038/s41598-019-50208-x T. Puzyn, B. Rasulev, A. Gajewicz, X. Hu, T.P. Dasari, A. Michalkova, H.-M. Hwang, A. Toropov, D. Leszczynska, J. Leszczynski, Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles, Nat. Nanotechnol., 6 (2011) 175. https://doi.org/10.1038/nnano.2011.10 Y. Bengio, O. Delalleau, C. Simard, Decision trees do not generalize to new variations, Comput. Intell., 26 (2010) 449-467. https://doi.org/10.1111/j.1467-8640.2010.00366.x H. Mei, Y. Zhou, G. Liang, Z. Li, Support vector machine applied in QSAR modelling, Chin. Sci. Bull., 50 (2005) 2291-2296. https://doi.org/10.1007/BF03183737 B. Saini, S. Srivastava, Nanotoxicity prediction using computational modelling - review and future directions, IOP Conf. Ser.: Mater. Sci. Eng., 348 (2018) 012005. https://doi.org/10.1088/1757-899X/348/1/012005 A.V. Singh, MHD Ansari, D. Rosenkranz, R. S. Maharjan, F. L. Kriegel, K. Gandhi, A. Kanas, R. Singh, P. Laux, A. Luch, Artificial intelligence and machine learning in computational nanotoxicology: unlocking and empowering nanomedicine, Adv. Heal. Mater., 17 (2020) 1901862. https://doi.org/10.1002/adhm.201901862 Tran, L., Banares, M.A., Rallo, R., Modelling the Toxicity of Nanoparticles; Springer: Berlin, 2017. U. Carlander, D. Li, O. Jolliet, C. Emond, G. Johanson, Toward a general physiologically-based pharmacokinetic model for intravenously injected nanoparticles, Int. J. Nanomed., 11 (2016) 625-640. https://doi.org/10.2147/IJN.S94370 Erdely, M. Dahm, B.T. Chen, P.C. Zeidler-Erdely, J.E. Fernback, M.E. Birch, D.E. Evans, M.L. Kashon, J.A. Deddens, T. Hulderman, Carbon nanotube dosimetry: from workplace exposure assessment to inhalation toxicology, Part. Fibre Toxicol., 10 (2013) 53. https://doi.org/10.1186/1743-8977-10-53 F.J. Miller, B. Asgharian, J.D. Schroeter, O. Price, Influence of alveolar mixing and multiple breaths of aerosol intake on particle deposition in the human lungs, J. Aerosol Sci., 99 (2016) 14. https://doi.org/10.1016/j.jaerosci.2022.106050 S. Anjilvel, B. Asgharian, ultiple-path model of particledeposition in the rat lung, Fundam. Appl. Toxicol., 28 (1995) 41. D.A. Winkler, E. Mombelli, A. Pietroiusti, L. Tran, A. Worth, B. Fadeel, M.J. McCall, Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential, Toxicology, 313 (2013) 15. https://doi.org/10.1016/j.tox.2012.11.005 L.M. Gilbertson, F. Melnikov, L.C. Wehmas, P.T. Anastas, R.L. Tanguay, J.B. Zimmerman, Toward safer multi-walled carbon nanotube design: Establishing a statistical model that relates surface charge and embryonic zebrafish mortality, Nanotoxicology, 10 (2016) 10-19. https://doi.org/10.3109/17435390.2014.996193 P. Schyman, R. Liu, V. Desai, A. Wallqvist, vNN Web Server for ADMET Predictions, Front. Pharmacol., 8 (2017) 1-14. https://doi.org/10.3389/fphar.2017.00889 H. Gonzalez-Diaz, ADMET-Multi-Output Cheminformatics Models for Drug Delivery, Interactomics, and Nanotoxicology, Curr. Drug Deli., (2016). https://pubmed.ncbi.nlm.nih.gov/27417300/ A.V. Singh, T. Jahnke, S. Wang, Y. Xiao, Y. Alapan, S. Kharratian, M.C. Onbasli, K. Kozielski, H. David, G. Richter, J. Bill, P. Laux, A. Luch, M. Sitti, Anisotropic Gold Nanostructures: Optimization via in Silico Modeling for Hyperthermia, ACS Appl. Nano Mater., 1 (2018) 6205-6216. https://doi.org/10.1021/acsanm.8b01406 A.V. Singh, T. Jahnke, Y. Xiao, S. Wang, Y. Yu, H. David, G. Richter, P. Laux, A. Luch, A. Srivastava, P.S. Saxena, J. Bill, M. Sitti, Peptide-Induced Biomineralization of Tin Oxide (SnO2) Nanoparticles for Antibacterial Applications, J. Nanosci. Nanotechnol., 19 (2019) 5674-5686. https://doi.org/10.1166/jnn.2019.16645 A.P. Toropova, A.A. Toropov, D. Leszczynska, J. Leszczynski, CORAL and Nano-QFAR: Quantitative feature – Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, CO3O4, and TiO2), Ecotoxicol. Environ. Saf., 139 (2017) 404-407. https://doi.org/10.1016/j.ecoenv.2017.01.054 R. Young, J. Ward, F. Scire, The Topografiner: An Instrument for Measuring Surface Microtopography, Rev. Sci. Instrum., 43 (1972) 999–1011. https://doi.org/10.1063/1.1685846 G. Binnig, H. Rohrer, C. Gerber, E. Weibel, Surface Studies by Scanning Tunneling Microscopy, Phys. Rev. Lett., 49 (1982) 57–61. https://doi.org/10.1103/PhysRevLett.49.57 C.I. Enriquez-Flores, J.J. Gervacio-Arciniega, E. Cruz-Valeriano, P. De Urquijo-Ventura, B.J. Gutierrez-Salazar, F.J. Espinoza-Beltran, Fast frequency sweeping in resonance-tracking SPM for high-resolution AFAM and PFM imaging, Nanotechnology, 23 (2012) 495705. https://doi.org/10.1088/0957-4484/23/49/495705 Y.Q. Xie, F. Liu, L. Huang, Lateral manipulation of small clusters on the Cu and Ag(1 1 1) surfaces with the single-atom and trimer-apex tips: Reliability study, Appl. Surf. Sci., 256 (2010) 4084–8. https://doi.org/10.1016/j.apsusc.2010.01.088 R. Miotto, F.D. Kiss, A.C. Ferraz, Changes in a nanoparticle's spectroscopic signal mediated by the local environment, Nanotechnology, 23 (2012) 485202. https://doi.org/10.1088/0957-4484/23/48/485202 A. Raman, J. Melcher, R. Tung, Cantilever dynamics in atomic force microscopy, Nano Today, 3 (2008) 20–27. https://doi.org/10.1016/S1748-0132(08)70012-4 B.J. Rodriguez, C. Callahan, S.V. Kalinin, R. Proksch, Dual-frequency resonance-tracking atomic force microscopy, Nanotechnology, 18 (2007) 475504. https://doi.org/10.1088/0957-4484/18/47/475504 Gómez, A. Gil, M. Álvarez, P. J. De Pablo, F. Herrero, I. Horcas, R. Sánchez, J. Colchero, J. Gómez and A. M. Baró, Scanning force microscopy three-dimensional modes applied to the study of the dielectric response of adsorbed DNA molecules, Nanotechnology, 13 (2002) 314–7. https://doi.org/10.1088/0957-4484/13/3/315 S. Jesse, S.V. Kalinin, R. Proksch, A.P. Baddorf, B.J. Rodriguez, The band excitation method in scanning probe microscopy for rapid mapping of energy dissipation on the nanoscale, Nanotechnology, 18 (2007) 32. https://doi.org/10.1088/0957-4484/18/43/435503 A.B. Kos, D.C. Hurley, Nanomechanical mapping with resonance tracking scanned probe microscope, Meas. Sci. Technol., 19 (2008) 015504. https://doi.org/10.1088/0957-0233/19/1/015504 M.P. Nikiforov, V.V. Reukov, G.L. Thompson, A.A. Vertegel, S. Guo, S.V. Kalinin, S. Jesse, Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response, Nanotechnology, 20 (2009) 405708. https://doi.org/10.1088/0957-4484/20/40/405708 D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagating errors, Nature, 323 (1986) 533–536.https://doi.org/10.1038/323533a0 O. Kwon, H. Kim, J. W. Kang, Energy exchange between vibration modes of a graphene nanoflake oscillator: Molecular dynamics study, Curr. Appl. Phys., 13 (2013) 789-794. https://doi.org/10.1016/j.cap.2013.11.027 E. Castellano-Hernández, F. Rodríguez, E. Serrano, P. Varona, G. Sacha, The use of artificial neural networks in electrostatic force microscopy, Nanoscale, Res. Lett., 7 (2012) 250. https://doi.org/10.1186/1556-276X-7-250 G. Sacha, Method to calculate electric fields at very small tip-sample distances in atomic force microscopy, Appl. Phys. Lett., 97 (2010) 033115. https://doi.org/10.1063/1.3467676 J. Arlat, Z. Kalbarczyk, T. Nanya, Nanocomputing: Small Devices, Large Dependability Challenges, IEEE Secur. Priv. Mag., 10 (2012) 69-72. https://doi.org/10.1109/MSP.2012.17 G. Tseng, J. Ellenbogen, Toward nanocomputers, Science, 294 (2001) 1293-1294. https://doi.org/10.1126/science.1066920 M. Uusitalo, J. Peltonen, T. Ryhänen, Machine Learning: How It Can Help Nanocomputing, J. Comput. Theor. Nanosci., 8 (2011) 1347–1363. https://doi.org/10.1166/jctn.2011.1821 P. Maurer, G. Kucsko, C. Latta, L. Jiang, N. Yao, S. Bennett, F. Pastawski, D. Hunger, N. Chisholm, M. Markham, D. Twitchen, J. Cirac, M. Lukin, Room-Temperature Quantum Bit Memory Exceeding One Second, Science, 336 (2012) 1283–1286. https://doi.org/10.1126/science.1220513 L. DeCastro, Fundamentals of natural computing: an overview, Phys. Life Rev., 4 (2007) 1–36. https://doi.org/10.1016/j.plrev.2006.10.002 M. Razzazi, M. Roayaei, Using sticker model of DNA computing to solve domatic partition, kernel and induced path problems, Inform. Sci., 181 (2011) 3581-3600. https://doi.org/10.1016/j.ins.2011.04.026 X. Zha, C. Yuan, Y. Zhang, Generalized criterion for a maximally multi-qubit entangled state, Laser Phys. Lett., 10 (2013) 045201. https://doi.org/10.1088/1612-2011/10/4/045201 J. Schmidhuber, Deep learning in neural networks: An overview, Neur. Net. 61 (2015) 85-117. https://doi.org/10.1016/j.neunet.2014.09.003 T. Cova, A. Pais, Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns, Front. Chem., 7 (2019) 809. https://doi.org/10.3389/fchem.2019.00809 J. Wang, S. Olsson, C. Wehmeyer, A. Pérez, N. Charron, G. Fabritiis, F. Noé, C. Clementi, Machine Learning of Coarse-Grained Molecular Dynamics Force Fields, ACS Cent. Sci., 5 (2019) 755–767. https://doi.org/10.1021/acscentsci.8b00913 A. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Žídek, A. Nelson, A. Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, P. Kohli, D. Jones, D. Silver, K. Hassabis, Improved protein structure prediction using potentials from deep learning, Nature, 577 (2020) 706–710. https://doi.org/10.1038/s41586-019-1923-7 D. Ahneman, J. Estrada, S. Lin, S. Dreher, A. Doyle, Predicting reaction performance in C–N cross-coupling using machine learning, Science, 360 (2018) 186–190. https://doi.org/10.1126/science.aar5169 M. Segler, M. Waller, Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction, Chem. Eur. J., 23 (2017) 5966–5971. https://doi.org/10.1002/chem.201605499 J. Donina, V. Dragone, De. Cronin, controlling an organic synthesis robot with machine learning to search for new reactivity, Nature, 559 (2018) 377–381. https://doi.org/10.1038/s41586-018-0307-8 M. Torrisi, G. Pollastri, Q. Le, Deep learning methods in protein structure prediction, Comput. Struct. Biotechnol. J., 18 (2020) 1301–1310. https://doi.org/10.1016/j.csbj.2019.12.011 N. Alwash, H. Kareem, Detection of COVID-19 Based on Chest Medical Imaging and Artificial Intelligence Techniques, Eng. Technol. J., 39 (2021) 1588–1600. https://doi.org/10.30684/etj.v39i10.2200 S. Raghunathan, U. Priyakumar, Molecular representations for machine learning applications in chemistry, Int. J. Quantum, Chem., 122 (2022) e26870. https://doi.org/10.1002/qua.26870 D. Cao, Y. Liang, Q. Xu, Molecular Descriptors Guide, Description of the Molecular Descriptors Appearing in the ChemoPy Software Package, China Computational Biology Drug Design, 2012. H. Yi, Visualized Co-Simulation of Adaptive Human Behavior and Dynamic Building Performance: An Agent-Based Model (ABM) and Artificial Intelligence (AI) Approach for Smart Architectural Design, Sustainability, 12(16) (2019) 6672. https://doi.org/10.3390/su12166672 V. Gautam, A. Gaurav, N. Masand, V. Sanghiran Lee, V.M. Patil, Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system, Mol Divers, 27 (2023) 959–985. https://doi.org/10.1007/s11030-022-10489-3 E. Pintelas, M. Liaskos, I. Livieris, S. Kotsiantis, P. Pintelas, Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction, J. Imaging., 6 (2020) 37. https://doi.org/10.3390/jimaging6060037 B. Mehrazma, A. Rauk, Exploring Amyloid-β Dimer Structure Using Molecular Dynamics Simulations, J. Phys. Chem. A, 123 (2019) 4658- 4670. https://doi.org/10.1021/acs.jpca.8b11251 J. Li, K. Lim, H. Yang, Z. Ren, S. Raghavan, P. Chen, T. Buonassisi, X. Wang, AI Applications Through the Whole Life Cycle of Material Discovery, Matter, 3 (2020) 393- 432. https://doi.org/10.1016/j.matt.2020.06.011 Y. Jia, X. Hou, Z. Wang, X. Hu, Machine Learning Boosts the Design and Discovery of Nanomaterials, ACS Sustainable Chem. Eng., 9 (2021) 6130 - 6147. https://doi.org/10.1021/acssuschemeng.1c00483 D. Rogers, M. Hahn, Extended-connectivity fingerprints, J. Chem. Inf. Model., 50 (2010) 742-754. https://doi.org/10.1021/ci100050t A. Karthikeyan, U. Priyakumar, Artificial intelligence: machine learning for chemical sciences, J. Chem. Sci., 134 (2022). https://doi.org/10.1007/s12039-021-01995-2 M. Butkiewicz, E. Lowe, R. Mueller, J. Mendenhall, P. Teixeira, C. Weaver, J. Meiler, Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database, Molecules, 18 (2012) 735-756. https://doi.org/10.3390/molecules18010735 A. Karthikeyan, U. Priyakumar, Artificial intelligence: machine learning for chemical sciences, J. Chem. Sci., 134 (2022) https://doi.org/10.1007/s12039-021-01995-2 B. Kim, S. Lee, J. Kim, Inverse Design of Porous Materials Using Artificial Neural Networks, Sci. Adv., 6 (2020) eaax9324. https://doi.org/10.1126/sciadv.aax9324 P. Mastracco, A. González-Rosell, J. Evans, P. Bogdanov, S. Copp, Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence, ACS Nano, 16 (2022) 16322–16331. https://doi.org/10.1021/acsnano.2c05390 F. Mekki-Berrada, Z. Ren, T. Huang, W. Wong, F. Zheng, J. Xie, I. Tian, S. Jayavelu, Z. Mahfoud, D. Bash, K. Hippalgaonkar, S. Khan, T. Buonassisi, Q. Li, X. Wang, Two-step machine learning enables optimized nanoparticle synthesis, Npj, Co mput. Mater., 7 (2021) 1–10. https://doi.org/10.1038/s41524-021-00520-w R. Rao, J. Carpena-Núñez, P. Nikolaev, M. Susner, K. Reyes, B. Maruyama, Advanced Machine Learning Decision Policies For Diameter Control Of Carbon Nanotubes, npj . Comput. Mater., 7 (2021) 157. https://doi.org/10.1038/s41524-021-00629-y H. Kim, J. Han, T. Han, Machine Vision-Driven Automatic Recognition of Particle Size And Morphology In SEM Images, Nanoscale, 12 (2020) 19461–19469. https://doi.org/10.1039/d0nr04140h Y. Liu, N. Marcella, J. Timoshenko, A. Halder, B. Yang, L. Kolipaka, M. Pellin, S. Seifert, S. Vajda, P. Liu, A. Frenkel, Mapping XANES Spectra On Structural Descriptors Of Copper Oxide Clusters Using Supervised Machine Learning, J. Chem. Phys., 151 (2019) 164201. https://doi.org/10.1063/1.5126597 J. Timoshenko, D. Lu, Y. Lin, A. Frenkel, Supervised Machine-Learning-Based Determination Of Three-Dimensional Structure Of Metallic Nanoparticles, J. Phys. Chem. Lett., 8 (2017) 5091–5098. https://doi.org/10.1021/acs.jpclett.7b02364 A. Belianinov, A. Ievlev, M. Lorenz, N. Borodinov, B. Doughty, S. Kalinin, F. Fernández, O. Ovchinnikova, Correlated Materials Characterization via Multi-modal Chemical and Functional Imaging, ACS Nano, 12 (2018) 11798–11818. https://doi.org/10.1021/acsnano.8b07292 S. Hong, C. Liow, J. Yuk, H. Byon, Y. Yang, E. Cho, J. Yeom, G. Park, H. Kang, S. Kim, et al., Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration, ACS Nano, 15 (2021) 3971–3995. https://doi.org/10.1021/acsnano.1c00211 J. Szargut, A. Ziebik, W. Stanek, Depletion of The Non-Renewable Natural Exergy Resources As A Measure Of The Ecological Cost, Energy Conv. Manag. , 43 (2002) 1149–1163. https://doi.org/10.1016/S0196-8904(02)00005-5 T. Kober, H. Schiffer, M. Densing, E. Panos, Global Energy Perspectives To 2060 − WEC's World Energy Scenarios 2019, Energy Strategy Reviews, 31 (2020) 100523. https://doi.org/10.1016/j.esr.2020.100523 D. Gielen, F. Boshell, D. Saygin, M. Bazilian, N. Wagner, R. Gorini, The Role of Renewable Energy In The Global Energy Transformation, Energy Strategy Reviews, 24 (2019) 38–50. https://doi.org/10.1016/j.esr.2019.01.006 N. Glanemann, S. Willner, A. Levermann, Paris Climate Agreement Passes The Cost-Benefit Test, Nat. Commun., 11 (2020) 110. https://doi.org/10.1038/s41467-019-13961-1 Y. Zhao, J. Yan, J. Yu, B. Ding, Advances in Nanofibrous Materials for Stable Lithium-Metal Anodes, ACS Nano, 16 (2022) 17891–17910. https://doi.org/10.1021/acsnano.2c09037 A. Chen, X. Zhang, L. Chen, S. Yao, Z. Zhou, A Machine Learning Model On Simple Features For CO2 Reduction Electrocatalysts, J. Phys. Chem. C, 124 (2020) 22471–22478. https://doi.org/10.1021/acs.jpcc.0c05964 A. Dubaish, A. Jaber, State-of-the-Art Review into Signal Processing and Artificial Intelligence-based Approaches Applied in Gearbox Defect Diagnosis, Eng. Technol. J., 42 (2024) 157-172. http://doi.org/10.30684/etj.2023.142462.1535 H. Rashid, K. Sultan, H. Anead, Thermal Performance of an Evacuated-Tube Solar Collector Using Nanofluids and an Electrical Curtain Controlled by an Artificial Intelligence Technique, Eng. Technol. J., 40 (2022) 8-19. http://doi.org/10.30684/etj.v40i1.2021 D. Rogers, M. Hahn, Extended-connectivity fingerprints, J. Chem. Inf. Model., 50 (2010) 742-754. https://doi.org/10.1021/ci100050t A. Karthikeyan, U. Priyakumar, Artificial intelligence: machine learning for chemical sciences, J. Chem. Sci., 134 (2022). https://doi.org/10.1007/s12039-021-01995-2 M. Butkiewicz, E. Lowe, R. Mueller, J. Mendenhall, P. Teixeira, C. Weaver, J. Meiler, Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database, Molecules, 18 (2012) 735-756. https://doi.org/10.3390/molecules18010735 M. Haghighatlari, J. Hachmann, Advances of machine learning in molecular modeling and simulation, Curr. Opin. Chem. Eng., 23 (2019) 51-57. https://doi.org/10.1016/j.coche.2019.02.009 C. Zhang, W. Wang, N. Xi, Y. Wang, L. Liu, Development and Future Challenges of Bio-Syncretic Robots, Engineering, 4 (2018) 452-463. https://doi.org/10.1016/j.eng.2018.07.005 H. Tao, T. Wu, M. Aldeghi, T. Wu, A. Aspuru-Guzik, E. Kumacheva, Nanoparticle Synthesis Assisted By Machine Learning, Nat. Rev. Mater., 6 (2021) 701–716. https://doi.org/10.1038/s41578-021-00337-5 O. Serradilla, E. Zugasti, C. Cernuda, A. Aranburu, J. Okariz, U. Zurutuza, Interpreting Remaining Useful Life estimations combining Explainable Artificial Intelligence and domain knowledge in industrial machinery, IEEE Int. Conf. Fuzzy Syst., 2020, 1-8. https://doi.org/10.1109/fuzz48607.2020.9177537 C. Vlek, H. Prakken, S. Renooij, B. Verheij, A Method for Explaining Bayesian Networks for Legal Evidence with Scenarios, Artif. Intell. Law., 24 (2016) 285-324. https://doi.org/10.1007/s10506-016-9183-4 M. Yeganejou, S. Dick, J. Miller, Interpretable Deep Convolutional Fuzzy Classifier, IEEE Trans. Fuzzy Syst., 28 (2020) 1407–1419. https://doi.org/10.1109/TFUZZ.2019.2946520 M. Islam, D. Anderson, A. Pinar, T. Havens, G. Scott, J. Keller, Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks, IEEE Trans. Fuzzy Syst., 28 (2020) 1291-1300. https://doi.org/10.1109/TFUZZ.2019.2917124 A. Jung, P. Nardelli, An Information-Theoretic Approach to Personalized Explainable Machine Learning, IEEE Signal. Process. Lett., 27 (2020) 825 - 829. https://doi.org/10.1109/LSP.2020.2993176 Q. Zheng, H. Delingette, N. Ayache, Explainable Cardiac Pathology Classification on Cine MRI with Motion Characterization by Semi-supervised Learning of Apparent Flow, Med. Image Anal., 56 (2019) 80 - 95. https://doi.org/10.1016/j.media.2019.06.001 R. Guidotti, A. Monreale, F. Giannotti, D. Pedreschi, S. Ruggieri, F. Turini, Factual and Counterfactual Explanations for Black Box Decision Making, IEEE Intel. Syst., 34 (2019) 14-23. https://doi.org/10.1109/MIS.2019.2957223 V. Gatta, V. Moscato, M. Postiglione, G. Sperlì, CASTLE: Cluster-Aided Space Transformation for Local Explanations, Expert. Syst. Appl., 179 (2021) 115045. https://doi.org/10.1016/j.eswa.2021.115045 J. Alonso, J. Toja-Alamancos, A. Bugarin, Experimental Study on Generating Multi-modal Explanations of Black-box Classifiers in terms of Gray-box Classifiers, IEEE Int. Conf. Fuzzy Syst., 2020, 1-8. https://doi.org/10.1109/fuzz48607.2020.9177770 H. Cao, R. Sarlin, A. Jung, Learning Explainable Decision Rules via Maximum Satisfiability, IEEE Access, 8 (2020) 218180 -218185. https://doi.org/10.1109/ACCESS.2020.3041040 M. Moradi, M. Samwald, Post-hoc Explanation of Black-box Classifiers using Confident Itemsets, Expert, Syst. Appl., 165 (2021) 113941. https://doi.org/10.1016/j.eswa.2020.113941 C. Rubio-Manzano, A. Segura-Navarrete, C. Martinez-Araneda, C. Vidal-Castro, Explainable hopfield neural networks using an automatic video-generation system, Appl. Sci., 11 (2021) 5771. https://doi.org/10.3390/app11135771 A. Mosavi, M. Salimi, S. Ardabili, T. Rabczuk, S. Shamshirband, A. Varkonyi-Koczy, State of the Art of Machine Learning Models in Energy Systems, a Systematic Review, Energies, 12 (2019) 1301. https://doi.org/10.3390/en12071301
Highlights
Simulating nanoscale systems is challenging due to the lack of real optical images. AI can enhance simulation quality and simplify analysis. Machine-aided design is essential for synthetic molecular design at the nanoscale. Renewable energy is vital for global sustainability and energy security. AI shows promise in developing efficient materials for power conversion and supply.
Recommended Citation
Eabd Alrida, Shahad; Obed, Ola; Taha, Elaf; Abdullah, Thamer; Hathal, Mustafa; and Somogyi, Viola
(2024)
"Applications of artificial intelligence in nanotechnology,"
Engineering and Technology Journal: Vol. 42:
Iss.
9, Article 3.
DOI: https://doi.org/10.30684/etj.2024.148957.1736
DOI
10.30684/etj.2024.148957.1736
First Page
1193
Last Page
1209





