IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-020-15190-3.html
   My bibliography  Save this article

A programmable chemical computer with memory and pattern recognition

Author

Listed:
  • Juan Manuel Parrilla-Gutierrez

    (University of Glasgow)

  • Abhishek Sharma

    (University of Glasgow)

  • Soichiro Tsuda

    (University of Glasgow)

  • Geoffrey J. T. Cooper

    (University of Glasgow)

  • Gerardo Aragon-Camarasa

    (University of Glasgow)

  • Kevin Donkers

    (University of Glasgow)

  • Leroy Cronin

    (University of Glasgow)

Abstract

Current computers are limited by the von Neumann bottleneck, which constrains the throughput between the processing unit and the memory. Chemical processes have the potential to scale beyond current computing architectures as the processing unit and memory reside in the same space, performing computations through chemical reactions, yet their lack of programmability limits them. Herein, we present a programmable chemical processor comprising of a 5 by 5 array of cells filled with a switchable oscillating chemical (Belousov–Zhabotinsky) reaction. Each cell can be individually addressed in the ‘on’ or ‘off’ state, yielding more than 2.9 × 1017 chemical states which arise from the ability to detect distinct amplitudes of oscillations via image processing. By programming the array of interconnected BZ reactions we demonstrate chemically encoded and addressable memory, and we create a chemical Autoencoder for pattern recognition able to perform the equivalent of one million operations per second.

Suggested Citation

  • Juan Manuel Parrilla-Gutierrez & Abhishek Sharma & Soichiro Tsuda & Geoffrey J. T. Cooper & Gerardo Aragon-Camarasa & Kevin Donkers & Leroy Cronin, 2020. "A programmable chemical computer with memory and pattern recognition," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15190-3
    DOI: 10.1038/s41467-020-15190-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-15190-3
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-020-15190-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Karimov, Artur & Kopets, Ekaterina & Karimov, Timur & Almjasheva, Oksana & Arlyapov, Viacheslav & Butusov, Denis, 2023. "Empirically developed model of the stirring-controlled Belousov–Zhabotinsky reaction," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Maćešić, Stevan & Čupić, Željko & Kolar-Anić, Ljiljana, 2023. "Effect of diffusion on steady state stability of an oscillatory reaction model," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    3. Fabian Schnitter & Benedikt Rieß & Christian Jandl & Job Boekhoven, 2022. "Memory, switches, and an OR-port through bistability in chemically fueled crystals," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    4. Ahmed A. Agiza & Kady Oakley & Jacob K. Rosenstein & Brenda M. Rubenstein & Eunsuk Kim & Marc Riedel & Sherief Reda, 2023. "Digital circuits and neural networks based on acid-base chemistry implemented by robotic fluid handling," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    5. Abhishek Sharma & Marcus Tze-Kiat Ng & Juan Manuel Parrilla Gutierrez & Yibin Jiang & Leroy Cronin, 2024. "A programmable hybrid digital chemical information processor based on the Belousov-Zhabotinsky reaction," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15190-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.