Discovery reveals ‘brain-like computing’ at the molecular level is possible

Mechanism of PCET. Credit: Natural materials (2022). DOI: 10.1038/s41563-022-01402-2

A discovery at the University of Limerick in Ireland has revealed for the first time that unconventional brain-like computing on the smallest scale of atoms and molecules is possible.

Researchers from the University of Limerick’s Bernal Institute have worked with an international team of scientists to create a new type of organic material that learns from its past behavior.

Discovery of ‘dynamic molecular switch’ that emulates synaptic behavior is revealed in 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 Science Foundation’s Pharmaceuticals Research Center of Ireland, hosted by UL, with Christian Nijhuis from the Center for Molecules and Brain-Inspired Nano Systems in University of Twente and Enrique del Barco from the University of Central Florida.

Working during lockdowns, the team developed a layer of molecules two nanometers thick, which is 50,000 times thinner than a strand of hair and remembers its history as electrons pass through it. .

Professor Thompson explained that “the switching probability and values ​​of on/off states are continuously 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 needed for deep learning, successfully mimicking Pavlovian “call and response” brain-like synaptic behavior.

Researchers demonstrated the new material properties using extensive experimental characterization and electrical measurements supported by multi-scale modeling ranging from predictive modeling of molecular structures at the quantum level to analytical mathematical modeling of electrical data.

To emulate the dynamic behavior of synapses at the molecular level, the researchers combined rapid electron transfer (similar to action potentials and rapid depolarization processes in biology) with slow diffusion-limited proton coupling (similar to role of biological calcium ions or neurotransmitters).

Since the steps of electron transfer and proton coupling inside the material occur at very different time scales, the transformation can emulate the plastic behavior of neural junctions synapses, Pavlovian learning and all the logic gates of digital circuits, simply by changing the applied voltage and duration. of voltage pulses during synthesis, they explained.

“It was a great lockdown project, with Chris, Enrique and I pushing each other through zoom meetings and gargantuan threads to bring our teams combined skills in modeling, synthesis and characterization. materials to the point where we could demonstrate these new brain properties,” Prof Thompson explained.

“The community has long known that silicon technology works completely differently from how our brains work, so we used new types of electronic materials based on soft molecules to emulate brain-like computer networks.”

The researchers explained that the method can in the future be applied to dynamic molecular systems driven by other stimuli such as light and coupled with different types of dynamic covalent bond formation.

This breakthrough opens up a whole new range of adaptive and reconfigurable systems, creating new opportunities in sustainable and green chemistry, from the more efficient production of pharmaceuticals and other value-added chemicals by flow chemistry to the development of new materials. organic materials for high-density computing and memory. storage in large data centers.

“This is just the beginning. We are already busy developing this next generation of smart molecular materials, which enables the development of sustainable alternative technologies to address major energy, environmental and health challenges,” explained Professor Thompson.