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
StatePublished - 21 Nov 2016
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada

Publication series

Name
PublisherIEEE Computer Society

Conference

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

Fingerprint

Nanoparticles
Genetic algorithms
Coulomb blockade
Charge transfer
Neural networks
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

Cite this

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. p. 5238-5245.

Research output: Scientific - peer-reviewConference contribution

@inbook{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",
month = "11",
doi = "10.1109/CEC.2016.7748354",
isbn = "978-1-5090-0623-6",
publisher = "IEEE Computer Society",
pages = "5238--5245",
booktitle = "2016 IEEE Congress on Evolutionary Computation (CEC 2016)",
address = "United States",

}

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, 24-29 July. DOI: 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: Scientific - peer-reviewConference contribution

TY - CHAP

T1 - A simulation tool for evolving functionalities in disordered nanoparticle networks

AU - van Damme,Rudolf M.J.

AU - Broersma,Haitze J.

AU - Mikhal,Julia Olegivna

AU - Lawrence,Celestine Preetham

AU - van der Wiel,Wilfred Gerard

N1 - 10.1109/CEC.2016.7748354

PY - 2016/11/21

Y1 - 2016/11/21

N2 - 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.

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.

KW - MSC-65Z05

KW - EWI-27454

KW - EC Grant Agreement nr.: FP7/317662

KW - Boolean logic

KW - Evolution-in-nanomaterio

KW - METIS-319492

KW - Monte Carlo

KW - Nanoparticle network

KW - Simulation

KW - Unconventional computation

KW - IR-102390

U2 - 10.1109/CEC.2016.7748354

DO - 10.1109/CEC.2016.7748354

M3 - Conference contribution

SN - 978-1-5090-0623-6

SP - 5238

EP - 5245

BT - 2016 IEEE Congress on Evolutionary Computation (CEC 2016)

PB - IEEE Computer Society

ER -

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. Available from, DOI: 10.1109/CEC.2016.7748354