Eabd Alrida, S., Obed, O., Taha, E., Abdullah, T., Hathal, M., Somogyi, V. (2024). Applications of artificial intelligence in nanotechnology. , 42(9), 1193-1209. doi: 10.30684/etj.2024.148957.1736
Shahad M. Eabd Alrida; Ola D. Obed; Elaf M. Taha; Thamer A. Abdullah; Mustafa M. Hathal; Viola Somogyi. "Applications of artificial intelligence in nanotechnology". , 42, 9, 2024, 1193-1209. doi: 10.30684/etj.2024.148957.1736
Eabd Alrida, S., Obed, O., Taha, E., Abdullah, T., Hathal, M., Somogyi, V. (2024). 'Applications of artificial intelligence in nanotechnology', , 42(9), pp. 1193-1209. doi: 10.30684/etj.2024.148957.1736
Eabd Alrida, S., Obed, O., Taha, E., Abdullah, T., Hathal, M., Somogyi, V. Applications of artificial intelligence in nanotechnology. , 2024; 42(9): 1193-1209. doi: 10.30684/etj.2024.148957.1736

Applications of artificial intelligence in nanotechnology

Engineering and Technology Journal
Volume 42, Issue 9, September 2024, Pages 1193-1209    PDF (1.41 M)
Document Type: Review Paper
DOI: 10.30684/etj.2024.148957.1736
Authors
Shahad M. Eabd Alrida1; Ola D. Obed1; Elaf M. Taha1; Thamer A. Abdullah* 1; Mustafa M. Hathal2; Viola Somogyi2
1Applied Sciences Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
2Asustainability Solutions Research Lab, Faculty of Engineering, University of Pannonia-Veszprém, Hungary.
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.

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.
Keywords
Nanomaterials; Machine Learning; Nanocomposites; Artificial Neural Networks; Molecular Design; Artificial intelligence; Nanotechnology; AI
References
  1. 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
  2. Mitchell, T. M. Machine learning; MA: McGraw-Hill, Boston, 1997.
  3. Bishop,C.M., Nasrabadi‏, N.M. Pattern Recognition and Machine Learning; Springer, New York, NY, USA, 2006.
  4. 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
  5. H. Shindo, Y. Matsumoto, Gated Graph Recursive Neural Networks for Molecular Property Prediction, https://doi.org/10.48550/arXiv.1909.00259
  6. Reisfeld, B. Mayeno, A.N. What is Computational Toxicology?; Humana Press: Springer Protocols, NJ, USA, 2012.
  7. 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
  8. 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
  9. 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
  10. 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.
  11. H. Kubinyi, From Narcosis to Hyperspace: The History of QSAR, Quant. Struct.-Act. Relat, 21 (2002) 348.
  12. 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
  13. 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
  14. A. Ben-Hur, D. Horn, H.T. Siegelmann, Support Vector Clustering, J. Mach. Learn. Res., 2 (2001) 125-137.
  15. 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
  16. 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
  17. T. Kohonen, Self-organized formation of topologically correct feature maps, Biol. Cybern., 43 (1982) 59–69. https://doi.org/10.1007/BF00337288
  18. M. Sato‏, A real time learning algorithm for recurrent analog neural networks, Biol. Cybern., 62 (1990) 237–241. https://doi.org/10.1007/BF00198098
  19. B.E. Boser, COLT '92: Proc. of the Fifth Annu.Workshop on Computational Learning Theory, Association for Computing Machinery: New York, 1992.
  20. C. Hansch, Quantitative approach to biochemical structure-activity relationships‏, Acc. Chem. Res.., 2 (1969) 232–239. https://doi.org/10.1021/ar50020a002
  21. 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
  22. Overtone, F. Applied Physiology: Including the Effects of Alcohol and Narcotics; American Book Company: New York, 1897.
  23. 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
  24. 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
  25. Hammett, L. P., Physical Organic Chemistry; McGraw-Hill: New York, 1940.
  26. M. Hosseini-Yeganeh, A.J. McLachlan, Antimicrob. Agents Chemother., 46 (2002) 2219-2228. https://doi.org/10.1128/AAC.46.7.2219-2228.2002
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. Tran, L., Banares, M.A., Rallo, R., Modelling the Toxicity of Nanoparticles; Springer: Berlin, 2017.
  37. 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
  38. 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
  39. 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
  40. S. Anjilvel, B. Asgharian, ultiple-path model of particledeposition in the rat lung, Fundam. Appl. Toxicol., 28 (1995) 41.
  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
  42. 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
  43. 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
  44. 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/
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. G. Tseng, J. Ellenbogen, Toward nanocomputers, Science, 294 (2001) 1293-1294. https://doi.org/10.1126/science.1066920
  65. 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
  66. 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
  67. 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
  68. 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
  69. 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
  70. 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
  71. 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
  72. 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
  73. 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
  74. 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
  75. 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
  76. 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
  77. 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
  78. 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
  79. 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
  80. 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.
  81. 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
  82. 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
  83. 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
  84. 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
  85. 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
  86. 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
  87. D. Rogers, M. Hahn, Extended-connectivity fingerprints, J. Chem. Inf. Model., 50 (2010) 742-754. https://doi.org/10.1021/ci100050t
  88. 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
  89. 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
  90. 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
  91. 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
  92. 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
  93. 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
  94. 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
  95. 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
  96. 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
  97. 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
  98. 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
  99. 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
  100. 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
  101. 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
  102. 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
  103. 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
  104. 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
  105. 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
  106. 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
  107. 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
  108. D. Rogers, M. Hahn, Extended-connectivity fingerprints, J. Chem. Inf. Model., 50 (2010) 742-754. https://doi.org/10.1021/ci100050t
  109. 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
  110. 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
  111. 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
  112. 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
  113. 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
  114. 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
  115. 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
  116. 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
  117. 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
  118. 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
  119. 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
  120. 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
  121. 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
  122. 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
  123. 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
  124. 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
  125. 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
  126. 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
Statistics
Article View: 801
PDF Download: 280


Journal Management System. Powered by iJournalPro.com