Applications of genetic algorithms in nanoscience : A short survey of recent results

• Autorzy: Paszkowicz W.

Warianty tytułu

PL

Zastosowania algorytmów genetycznych w dziedzinie nanomateriałów : krótki przegląd ostatnich wyników

• Języki publikacji: EN

Abstrakty

EN

Global-search procedures are an important element of materials design, processing, and properties determination. Genetic algorithms, a subset of algorithms based on evolutionary computation methods, are an example of global optimization methods inspired by biological principles of evolution. In materials science and related fields of science and technology, these algorithms are successfully used, e.g., for optimization of material elaboration and for design of materials with desired physical or structural properties, in working out modern devices based on specific physical principles, as well as in elaboration of improved methods of materials characterization. Nanomaterials science is a rapidly growing subfield of materials science covering various objects characterized by nanometric size. In this review, examples of recent applications of genetic algorithms in nanomaterials science are presented. Representative examples illustrate how useful are such computational methods for solving the scientific tasks, e.g., for thin-film growth modeling and characterization, for optimization of quantum-dot systems and of nanoparticle based medical therapies, for design of hard nanocomposite materials and for optimization of nanomaterial-based optical nanodevices and sensors of various gases

PL

Procedury przeszukiwania globalnego są ważnym elementem projektowania materiałów, ich wytwarzania i określania właściwości. Algorytmy genetyczne, stanowiące podzbiór algorytmów opartych o obliczenia ewolucyjne, stanowią przykład metody optymalizacji globalnej inspirowanej przez biologiczne prawa ewolucji. W nauce o materiałach i w pokrewnych dziedzinach nauki i techniki, algorytmy genetyczne są z powodzeniem stosowane, na przykład, w celu optymalizacji wytwarzania, przy projektowaniu materiałów o pożądanych właściwościach fizycznych lub strukturalnych, w opracowaniu nowoczesnych urządzeń działających na podstawie określonych zasad fizycznych, jak również w udoskonalaniu metod charakteryzacji materiałów. Nauka o nanomateriałach jest szybko rozwijającą się dziedziną materiałoznawstwa obejmującą różnorodne obiekty charakteryzujące się rozmiarami w skali nano. W niniejszym przeglądzie zaprezentowano przykłady ostatnio opisanych zastosowań algorytmów genetycznych w nauce o nanomateriałach. Reprezentatywne przykłady ilustrują, jak przydatne są takie metody obliczeniowe w rozwiązywaniu zadań naukowych, np. dla celów modelowania wzrostu cienkich warstw i dla ich charakteryzacji, dla optymalizacji układów kropek kwantowych i terapii medycznych bazujących na nanocząstkach, dla projektowania materiałów nanokompozytowych o wysokiej twardości i dla optymalizacji nanourządzeń optycznych i czujników różnego rodzaju gazów.

Słowa kluczowe

EN

genetic algorithm; application; evolution; optimization; artificial intelligence; prediction; parallel computing; global search; modeling; materials; science; engineering

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Methods in Materials Science
• Rocznik: 2013
• Tom: Vol. 13, No. 1
• Strony: 127--134
• Opis fizyczny: Bibliogr. 59 poz., rys.

Twórcy

autor

Paszkowicz W.

• Institute of Physics, Polish Academy of Sciences, al. Lotnikow 32/46, 02-668 Warsaw, Poland

Bibliografia

• Affenzeller, M., Wagner, S., 2009, Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications, Chapman & Hall/CRC, Boca Raton, FL, USA.
• Alander, J.T., 2000, An Indexed Bibliography of Genetic Algorithms in Chemistry and Physics, Rep. 94-1-ChemPhys, University of Vaasa, Vaasa, Finland, 118 pages.
• Alander, J.T., 2008, An Indexed Bibliography of Genetic Algorithms in Materials Science and Engineering, Rep. 94-1-MSE, University of Vaasa, Vaasa, Finland, ftp.uwasa.fidirectory cs/report94-1, gaMSEbib.pdf.
• Amirjalayer, S., Snurr, R.Q., Schmid, R., 2012, Prediction of Structure and Properties of Boron-based Covalent Organic Frameworks by a First-principles Derived Force Field, J. Phys. Chem. C, 116, 4921-4929.
• Arabas, J., 2004, Wykłady z algorytmów ewolucyjnych (Lectures on Evolutionary Algorithms), Wydawnictwa Naukowo-Techniczne, Warsaw (in Polish).
• Baeck, T., Fogel, D., Michalewicz, Z., 1997, Handbook of Evolutionary Computation, Oxford University Press and Institute of Physics, Oxford and London.
• Bath, J., Turberfield, A.J., 2007, DNA Nanomachines, Nature Nanotechnol., 2, 275-228.
• Baumes, L.A., Krüger, F., Jimenez, S., Collet, P., Corma, A., 2011, Boosting Theoretical Zeolitic Framework Generation for the Determination of New Materials Structures Using GPU Programming. Phys. Chem. Chem. Phys., 13, 4674-4678.
• Björck, M., Andersson, G., 2007, GenX: An Extensible X-ray Reflectivity Refinement Program Utilizing Differential Evolution, J. Appl. Crystallogr., 40, 1174-1178.
• Bleicher, L., Sasaki, J.M., Orloski, R.V., Cardoso, L.P., Hayashi, M.A., Swart, J.W., 2004, IonRock: Software for Solving Strain Gradients of Ion-Implanted Semiconductors by X-ray Diffraction Measurements and Evolutionary Programming, Comput. Phys. Commun., 160,158-165.
• Çamkerten, R., Erdoğan, S., Altınten, A., Alpbaz, M., 2012, Improvement in Properties of Nanocomposite Materials by Temperature Control, Indian J. Chem. Technol., 19, 185-190.
• Chakraborti, N., 2004, Genetic Algorithms in Materials Design and Processing, Int. Mater. Rev., 49, 246-260.
• Chiesa, M., Rigoni, F., Paderno, M., Borghetti, P., Gagliotti, G., Bertoni, M., Ballarin Denti, A., Schiavina, L., Goldoni, A., Sangaletti, L., 2012, Development of Low-cost Ammonia Gas Sensors and Data Analysis Algorithms to Implement a Monitoring Grid of Urban Environmental Pollutants, J. Environ. Monit., 14, 1565-1575.
• Cho, E.N., Moon, P., Kim, C.E., Yun, I., 2012, Modeling and Optimization of ITO/Al/ITO Multilayer Films Characteristics Using Neural Network and Geneticalgorithm, Expert Syst. Applic., 39, 8885-8889,
• Corrigan, P., Lwin, M., Gross, B., Moshary, F., 2009, Assessment of Dual Ammonia and Ozone Open-path Sensing with a Quantum Cascade Laser, Proc.Conf. International Quantum Electronics Conference, Baltimore, Maryland, OSA Technical Digest (CD), paper JWA66.
• Dash, S., Chakraborty, G., Sengupta, A., 2012, Sarkar, CK, J. Comput. Theor. Nanosci., 9, 434-440.
• d'Avezac, M., Luo, J.W., Chanier, T., Zunger, A., 2012, Genetic-algorithm Discovery of a Direct-gap and Optically Allowed Superstructure from Indirect-gap Si and Ge Semiconductors, Phys. Rev. Lett., 108, 027401.
• Day, G.M., Cooper, T.G., Cruz-Cabeza, A.J., Hejczyk, K.E., Ammon, H.L., Boerrigter, S.X.M., Tan, J.S., Della Valle, R.G., Venuti, E., Jose, J., Gadre, S.R., Desiraju, G.R., Thakur, T.S., van Eijck, B.P., Facelli, J.C., Bazterra, V.E., Ferraro, M.B., Hofmann, D.W.M., Neumann, M.A., Leusen, F.J.J., Kendrick, J., Price, S.L., Misquitta, A.J., Karamertzanis, P.G., Welch, G.W.A., Scheraga, H.A., Arnautova, Y.A., Schmidt, M.U., van de Streek, J., Wolf, A.K., Schweizer, B., 2009, Significant Progress in Predicting the Crystal Structures of Small Organic Molecules – a Report on the Fourth Blind Test, Acta Crystallogr. B, 65, 107-125.
• Dudiy, S.V., Zunger, A., 2006, Searching for Alloy configurations with Target Physical Properties: Impurity Design via a Genetic Algorithm Inverse Band Structure Approach, Phys. Rev. Lett., 97, 046401.
• Forestiere, C., Pasquale, A.J., Capretti, A., Miano, G., Tamburrino, A., Lee, S.Y., Reinhard, B.M., Dal Negro, L., 2012, Genetically Engineered Plasmonic Nanoarrays, Nano Lett., 12, 2037-2044.
• Goldberg, D.E., 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, USA.
• Gómez-Campos, F.M., Rodríguez-Bolívar, S., de Jong van Coevorden, C.M., Luque-Rodríguez, A., Lara-Bullejos, P., Carceller, J.E., 2009, Bandgap Calculation in Si Quantum Dot Arrays Using a Genetic Algorithm, Physica E: Low-dimens. Syst. Nanostruct., 41, 1712-1717.
• Grigorenko, I., Speer, O., Garcia, M.E., 2002, Coherent Control of Photon-assisted Tunneling between Quantum Dots: A Theoretical Approach Using Genetic Algorithms, Phys. Rev. B, 65, 235309.
• Hageman, J.A., 2004, Optimal Optimization in Chemometrics, Ph.D. Thesis, Katholieke Universiteit Nijmegen, Nijmegen. Holland, J.H., 1975, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, USA.
• Jóhannesson, G.H., Bligaard, T., Ruban, A.V., Skriver, H. L., Jacobsen, K.W., Nørskov, J. K., 2002, Combined Electronic Structure and Evolutionary Search Approach to Materials Design, Phys. Rev. Lett., 88, 255506-1-5.
• Johnston, R.L., 2003, Evolving Better Nanoparticles: Genetic Algorithms for Optimising Cluster Geometries, Dalton Trans., 22, 4193-4207.
• Kim, K.J., Cho, S.B., 2006, A Comprehensive Overview of the Applications of Artificial Life, Artif. Life, 12, 153-182.
• Ko, Y.-D., Moon, P., Kim, C.E., Ham, M.-H., Myoung, J.-M., Yun, I., 2009, Modeling and Optimization of the Growth Rate for ZnO thin Films Using Neural Networks and Genetic Algorithms, Expert Syst. Applic., 36, 4061-4066.
• Koljonen, J., Nordling, T., Alander, J., 2008, A Review of Genetic Algorithms in Near Infrared Spectroscopy and Chemometrics: Past and Future, J. Near Infrared Spectrosc., 16, 189-197.
• Korkusinski, M., Zielinski, M., Hawrylak, P., 2009, Multiexciton Complexes in InAs Self-assembled Quantum Dots, J. Appl. Phys., 105, 122406.
• Kovacic, S., Samii, L., Woolfson, D.N., Curmi, P.M.G., Linke, H., Forde, N.R., Blab, G.A., 2012, Design and Construction of a One-dimensional DNA Track for an Artificial Molecular Motor, J. Nanomater., 109238-1-10.
• Krink, T., Vollrath, F., 1997, Analysing Spider Web-building Behaviour with Rule-based Simulations and Genetic Algorithms, J. Theor. Biol., 185, 321-331.
• Leardi, R., 2001, Genetic Algorithms in Chemometrics and Chemistry: A Review, J. Chemometrics, 15, 559-569.
• Luke, B.T., 2003, Applying Genetic Algorithms and Neural Networks to Chemometric Problems, Nature-Inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks, eds, Leardi, R., Elsevier, New York, 3-54.
• Luo, L.Q., 2006, Chemometrics and Its Applications to X-ray Spectrometry. X-Ray Spectrom., 35, 215-225.
• Maier, W.F., Stöwe K., Sieg S., 2007, Combinatorial and Highthroughput Materials Science, Angew. Chem. Int, 46, 6016-6067.
• Meng, Z., Taferner, W.T., Yang, Q., Eying, K.G., Pao, Y.-H., 1998a, Computational Intelligence Procedures for Monitoring and Control of MBE Growth of Arbitrary Film Structures, Artificial Intelligence in Real-Time Control, eds, Pao, Y.H., LeClair, S.R., Elsevier, Kidlington, UK, 155-166.
• Meng, Z., Yang, Q., Yip, P.C., Eyink K.G., Taferner, W.T., Igelnik, B., 1998b, Combined Use of Computational Intelligence and Materials Data for On-line Monitoring and Control of MBE Experiments, Eng. Applic. Artif. Intell., 11, 587-595.
• Mital, M., Tafreshi, H.V., 2012, A Methodology for Determining Optimal Thermal Damage in Magnetic Nanoparticle Hyperthermia cancer Treatment, Int. J. Numer. Meth. Biomed. Engng., 28, 205-213.
• Mitra, K., 2008, Genetic Algorithms in Polymeric Material Production, Design, Processing and Other Applications: A Review, Int. Mater. Rev., 53, 275-297.
• Oganov, A.R., Glass, C.W., 2008, Evolutionary Crystal Structure Prediction as a Tool in Materials Design, J. Phys.: Condens. Matter, 20, 064210.
• Oganov, A.R., Lyakhov,A.O., Valle, M., 2011a, How Evolutionary Crystal Structure Prediction Works—and Why, Acc. Chem. Res., 44, 227-237.
• Oganov, A.R., Tipton, W.W., Hennig, R.G., 2011b, Modern Methods of Crystal Structure Prediction, Wiley-VCH. Weinheim, Germany
• Passaro, A., Tanaka, R.Y., Muraro, A. Jr., Soares Vieira, G., Abe, N.M., 2010, Self-consistent Optimization of Multi-Quantum Well Structures by a Genetic Algorithm, IEEE Trans. Magnet., 46, 2759-2762.
• Paszkowicz, W., 2009, Genetic Algorithms, a Nature-inspired Tool: Survey of Applications in Materials Science and Related Fields. Mater. Manuf. Process., 24, 174-197.
• Paszkowicz, W., 2013, Genetic Algorithms, a Nature-inspired Tool: Survey of Applications in Materials Science and Related Fields – part II, Mater. Manuf. Process., accepted.
• Piquini, P., Graf, P.A, Zunger, A., 2008, Band-gap Design of Quaternary (In,Ga)(As,Sb) Semiconductors via the Inverse-band-structure Approach, Phys. Rev. Lett., 100,186403.
• Reibold, M., Paufler, P., Levin, A.A., Kochmann, W., Pätzke, N. Meyer, D.C. 2006, Materials: Carbon Nanotubes in an Ancient Damascus Sabre, Nature, 444, 286-286.
• Sastry, K., 2007, Genetic Algorithms and Genetic Programming for Multiscale Modeling: Applications in Materials Science and Chemistry and Advances in Scalability, PhD Thesis, University of Illinois at Urbana-Champaign, Urbana, Illinois.
• Tafipolsky, M., Schmid, R., 2009, Systematic First Principles Parameterization of force Fields for Metal-organic Frameworks Using a Genetic Algorithm Approach, J. Phys. Chem. B, 113, 1341-1352.
• Takahashi, T., Funatsu, K., Ema, Y., 2005, Automatic Modelling of Reaction Systems Using Genetic Algorithms and Its Application to Chemical Vapour Deposition Processes: Advanced Utilizations of Simulators for Chemical Systems. Meas. Sci. Technol., 16, 278-284.
• Takemiya, H, Tanaka, Y., Sekiguchi S., Ogata, S., Kalia, R.K., Nakano, A., Vashishta, P., 2006, Sustainable Adaptive Grid Supercomputing: Multiscale Simulation of Semiconductor Processing Across the Pacific. Proc. ACM/IEEE Supercomputing 2006 Conf. (SC'06), Tampa, USA, Nov. 11-17 2006, 1-11.
• Thalken, J., Haas, S., Levi, A.F.J., 2005, Synthesis for Semiconductor Device Design, J. Appl. Phys., 98, 044508.
• van der Wiel, W.G., De Franceschi, S., Elzerman, J.M. Fujisawa, T. Tarucha, S.L,. Kouwenhoven, P., 2002, Electron Transport through Double Quantum Dots, Rev. Mod. Phys., 75, 1-22.
• Wang, B., Yin, S., Wang, G., Zhao, J., 2001, Structures and Electronic Properties of Ultrathin Titanium Nanowires. J. Phys.: Condens. Matter, 13, L403-L408.
• Woodley, S.M., Catlow, R., 2008, Crystal Structure Prediction from First Principles, Nature Mater., 7, 937-946.
• Ye, Z., Li, Z., Xie, M., 2010, Some Improvements on Adaptive Genetic Algorithms for Reliability-Related Applications, Reliab. Eng. Syst. Safety, 95, 120-126.
• Yusup, N., Zain, A.M., Hashim, S.Z.M., 2012, Evolutionary Techniques in Optimizing Machining Parameters: Review and Recent Applications (2007–2011), Expert Syst. Applic., 39, 9909-9927.
• Zhong, P., He, G., Zhang, M., 2012, Optimal Spectra of White Light-emitting Diodes Using Quantum Dot Nanophosphors, Optics Express, 20, 9122-9134.