A simulation tool for evolving functionalities in disordered nanoparticle networks

Rudolf M.J. van Damme, Haitze J. Broersma, Julia Olegivna Mikhal, Celestine Preetham Lawrence, Wilfred Gerard van der Wiel

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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    Abstract

    Recently published experimental work on evolution-in-materio applied to nanoscale materials shows promising results for future reconfigurable devices. These experimental results are based on disordered nanoparticle networks, without a predefined design. The material is treated as a black-box, and genetic algorithms are used to find appropriate configuration voltages to enable a targeted functionality. To support future experimental work, we developed simulation tools for predicting candidate functionalities. One of these tools is based on a neural network model, but the one presented here is based on a physical model. The physical model describes the charge transport between the nanoparticles, which is governed by what is known as the Coulomb blockade effect. The new simulation tool combines a genetic algorithm with Monte-Carlo simulations that are based on this physical model. The code of the new simulation tool has been validated with known results on small deterministically designed nanoparticle networks from literature. The code has also been applied to simulate reconfigurable logic in small k×k grids of nanoparticles. The results show that the new approach has great potential for partly replacing costly and time-consuming experiments.
    Original languageUndefined
    Title of host publication2016 IEEE Congress on Evolutionary Computation (CEC 2016)
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Pages5238-5245
    Number of pages8
    ISBN (Print)978-1-5090-0623-6
    DOIs
    Publication statusPublished - 21 Nov 2016
    Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver Convention Centre, Vancouver, Canada
    Duration: 24 Jul 201629 Jul 2016

    Publication series

    Name
    PublisherIEEE Computer Society

    Conference

    Conference2016 IEEE Congress on Evolutionary Computation, CEC 2016
    Abbreviated titleCEC
    CountryCanada
    CityVancouver
    Period24/07/1629/07/16

    Keywords

    • MSC-65Z05
    • EWI-27454
    • EC Grant Agreement nr.: FP7/317662
    • Boolean logic
    • Evolution-in-nanomaterio
    • METIS-319492
    • Monte Carlo
    • Nanoparticle network
    • Simulation
    • Unconventional computation
    • IR-102390

    Cite this

    van Damme, R. M. J., Broersma, H. J., Mikhal, J. O., Lawrence, C. P., & van der Wiel, W. G. (2016). A simulation tool for evolving functionalities in disordered nanoparticle networks. In 2016 IEEE Congress on Evolutionary Computation (CEC 2016) (pp. 5238-5245). USA: IEEE Computer Society. https://doi.org/10.1109/CEC.2016.7748354
    van Damme, Rudolf M.J. ; Broersma, Haitze J. ; Mikhal, Julia Olegivna ; Lawrence, Celestine Preetham ; van der Wiel, Wilfred Gerard. / A simulation tool for evolving functionalities in disordered nanoparticle networks. 2016 IEEE Congress on Evolutionary Computation (CEC 2016). USA : IEEE Computer Society, 2016. pp. 5238-5245
    @inproceedings{86ccf37cafa2410888d4bf81a9b42e5d,
    title = "A simulation tool for evolving functionalities in disordered nanoparticle networks",
    abstract = "Recently published experimental work on evolution-in-materio applied to nanoscale materials shows promising results for future reconfigurable devices. These experimental results are based on disordered nanoparticle networks, without a predefined design. The material is treated as a black-box, and genetic algorithms are used to find appropriate configuration voltages to enable a targeted functionality. To support future experimental work, we developed simulation tools for predicting candidate functionalities. One of these tools is based on a neural network model, but the one presented here is based on a physical model. The physical model describes the charge transport between the nanoparticles, which is governed by what is known as the Coulomb blockade effect. The new simulation tool combines a genetic algorithm with Monte-Carlo simulations that are based on this physical model. The code of the new simulation tool has been validated with known results on small deterministically designed nanoparticle networks from literature. The code has also been applied to simulate reconfigurable logic in small k×k grids of nanoparticles. The results show that the new approach has great potential for partly replacing costly and time-consuming experiments.",
    keywords = "MSC-65Z05, EWI-27454, EC Grant Agreement nr.: FP7/317662, Boolean logic, Evolution-in-nanomaterio, METIS-319492, Monte Carlo, Nanoparticle network, Simulation, Unconventional computation, IR-102390",
    author = "{van Damme}, {Rudolf M.J.} and Broersma, {Haitze J.} and Mikhal, {Julia Olegivna} and Lawrence, {Celestine Preetham} and {van der Wiel}, {Wilfred Gerard}",
    note = "10.1109/CEC.2016.7748354",
    year = "2016",
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    language = "Undefined",
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    van Damme, RMJ, Broersma, HJ, Mikhal, JO, Lawrence, CP & van der Wiel, WG 2016, A simulation tool for evolving functionalities in disordered nanoparticle networks. in 2016 IEEE Congress on Evolutionary Computation (CEC 2016). IEEE Computer Society, USA, pp. 5238-5245, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 24/07/16. https://doi.org/10.1109/CEC.2016.7748354

    A simulation tool for evolving functionalities in disordered nanoparticle networks. / van Damme, Rudolf M.J.; Broersma, Haitze J.; Mikhal, Julia Olegivna; Lawrence, Celestine Preetham; van der Wiel, Wilfred Gerard.

    2016 IEEE Congress on Evolutionary Computation (CEC 2016). USA : IEEE Computer Society, 2016. p. 5238-5245.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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    AB - Recently published experimental work on evolution-in-materio applied to nanoscale materials shows promising results for future reconfigurable devices. These experimental results are based on disordered nanoparticle networks, without a predefined design. The material is treated as a black-box, and genetic algorithms are used to find appropriate configuration voltages to enable a targeted functionality. To support future experimental work, we developed simulation tools for predicting candidate functionalities. One of these tools is based on a neural network model, but the one presented here is based on a physical model. The physical model describes the charge transport between the nanoparticles, which is governed by what is known as the Coulomb blockade effect. The new simulation tool combines a genetic algorithm with Monte-Carlo simulations that are based on this physical model. The code of the new simulation tool has been validated with known results on small deterministically designed nanoparticle networks from literature. The code has also been applied to simulate reconfigurable logic in small k×k grids of nanoparticles. The results show that the new approach has great potential for partly replacing costly and time-consuming experiments.

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    van Damme RMJ, Broersma HJ, Mikhal JO, Lawrence CP, van der Wiel WG. A simulation tool for evolving functionalities in disordered nanoparticle networks. In 2016 IEEE Congress on Evolutionary Computation (CEC 2016). USA: IEEE Computer Society. 2016. p. 5238-5245 https://doi.org/10.1109/CEC.2016.7748354