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Digital circuits and neural networks based on acid-base chemistry implemented by robotic fluid handling

Author

Listed:
  • Ahmed A. Agiza

    (Brown University)

  • Kady Oakley

    (Brown University)

  • Jacob K. Rosenstein

    (Brown University)

  • Brenda M. Rubenstein

    (Brown University)

  • Eunsuk Kim

    (Brown University)

  • Marc Riedel

    (University of Minnesota)

  • Sherief Reda

    (Brown University)

Abstract

Acid-base reactions are ubiquitous, easy to prepare, and execute without sophisticated equipment. Acids and bases are also inherently complementary and naturally map to a universal representation of “0” and “1.” Here, we propose how to leverage acids, bases, and their reactions to encode binary information and perform information processing based upon the majority and negation operations. These operations form a functionally complete set that we use to implement more complex computations such as digital circuits and neural networks. We present the building blocks needed to build complete digital circuits using acids and bases for dual-rail encoding data values as complementary pairs, including a set of primitive logic functions that are widely applicable to molecular computation. We demonstrate how to implement neural network classifiers and some classes of digital circuits with acid-base reactions orchestrated by a robotic fluid handling device. We validate the neural network experimentally on a number of images with different formats, resulting in a perfect match to the in-silico classifier. Additionally, the simulation of our acid-base classifier matches the results of the in-silico classifier with approximately 99% similarity.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36206-8
    DOI: 10.1038/s41467-023-36206-8
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    References listed on IDEAS

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    2. Christopher E. Arcadia & Eamonn Kennedy & Joseph Geiser & Amanda Dombroski & Kady Oakley & Shui-Ling Chen & Leonard Sprague & Mustafa Ozmen & Jason Sello & Peter M. Weber & Sherief Reda & Christopher , 2020. "Multicomponent molecular memory," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
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