A discovery at the University of Limerick in Ireland has revealed for the first time that unconventional brain-like computing at the smallest scale of atoms and molecules is possible.
Researchers from the University of Limerick’s Bernal Institute teamed up with an international team of scientists to create a new type of organic material that learns from its past behavior.
The discovery of the “dynamic molecular switch” that mimics synaptic behavior is revealed in a new study in the journal Natural materials.
The study was led by Damien Thompson, professor of molecular modeling in UL’s Department of Physics and director of SSPC, the UL-hosted Science Foundation Ireland Research Center for Pharmaceuticals, along with Christian Nijhuis of the Center for Molecules and Brain-Inspired Nano Systems at the University of Twente and Enrique del Barco at the University of Central Florida.
Working during lockdowns, the team developed a two-nanometer thick layer of molecules, which is 50,000 times thinner than a strand of hair and remembers its history when electrons pass through it.
Professor Thompson explained that the “switching probability and the values of the on/off states are constantly changing in the molecular material, providing a disruptive new alternative to conventional silicon-based digital switches that can only be on or off.”
The newly discovered dynamic organic switch displays all the mathematical logic functions required for deep learningsuccessfully mimic Pavlovian synaptic brain-like behavior of “call and response”.
The researchers demonstrated the new material properties using extensive experimental characterization and electrical measurements, supported by multiscale modeling ranging from predictive modeling of the molecular structures at the quantum level to analytical mathematical modeling of the electrical data.
To study the dynamic behavior of synapses at the molecular levelthe researchers combined fast electron transfer (similar to action potentials and fast depolarization processes in biology) with slow proton coupling limited by diffusion (similar to the role of biological calcium ions or neurotransmitters).
Because the electron transfer and proton coupling steps in the material occur on very different time scales, the transformation can mimic the plastic behavior of synapse-neuronal junctions, Pavlovian learning, and all logic gates for digital circuits, simply by applied voltage and the duration of voltage pulses during synthesis, they explained.
“This has been a great lockdown project, with Chris, Enrique and I pushing each other through Zoom meetings and giant email threads to bring our teams combined materials modeling, synthesis and characterization skills to the point where we could run these new brain-like computers. demonstrate properties,” explains Professor Thompson.
“The community has long known that silicon technology works completely differently from how our brains work, and so we’ve been using new types of electronic materials based on soft molecules to mimic brain-like computer networks.”
The researchers explained that in the future, the method could be applied to dynamic molecular systems driven by other stimuli such as light and linked to different types of dynamic covalent bond formation.
This breakthrough opens up a whole new range of adaptable and reconfigurable systems, creating new opportunities in sustainable and green chemistry, from more efficient flow chemistry production of pharmaceuticals and other value-added chemicals to development of new organic materials for high-density computing and memory storage in large data centers.
“This is just the beginning. We are already expanding this next generation of intelligent molecular materials, which will enable the development of sustainable alternative technologies to address major energy, environmental and health challenges,” explains Professor Thompson.
Enrique del Barco, Dynamic molecular switches with hysteretic negative differential conductance mimicking synaptic behavior, Natural materials (2022). DOI: 10.1038/s41563-022-01402-2
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