Printing circuits on rare nanomagnets brings a new twist to computing

Newswise – New research artificially creating a rare form of matter known as spin glass could spark a new paradigm in artificial intelligence by allowing algorithms to be directly printed as physical material. The unusual properties of spin glass enable a form of AI that can recognize objects from partial images, just like the brain does, and show promise for low-power computing, among other capabilities intriguing.

“Our work has resulted in the first experimental realization of an artificial spin glass composed of nanomagnets arranged to replicate a neural network,” said Michael Saccone, postdoctoral researcher in theoretical physics at Los Alamos National Laboratory and lead author of new paper in Nature Physics. “Our paper lays the foundation we need to use these physical systems in a practical way.”

Rotating glasses are a way to think mathematically about the structure of materials. Being free, for the first time, to modify the interaction within these systems using electron beam lithography makes it possible to represent a variety of computational problems in spin glass networks, Saccone said.

At the intersection of engineering materials and computation, spin glass systems are a type of disordered system of nanomagnets resulting from random interactions and competition between two types of magnetic order in the material. They exhibit “frustration”, meaning that they do not settle into a uniformly ordered configuration when their temperature drops, and they possess distinct thermodynamic and dynamic characteristics that can be exploited for computational applications.

“Theoretical models describing spin glasses are widely used in other complex systems, such as those describing brain function, error-correcting codes, or stock market dynamics,” Saccone said. “This broad interest in spin glasses provides strong motivation to generate an artificial spin glass.”

The research team combined theoretical and experimental work to fabricate and observe artificial spin glass as a proof-of-principle Hopfield neural network, which mathematically models associative memory to guide the disorder of artificial spin systems.

The Spin Glass and Hopfield networks developed in symbiosis, one feeding on the other. Associative memory, whether in a Hopfield network or other forms of neural networks, connects two or more object-related memory patterns. If only one memory is triggered, for example by receiving a partial image of an input face, then the network can recall the full face. Unlike more traditional algorithms, associative memory does not require a perfectly identical scenario to identify a memory.

The memories of these networks correspond to the ground states of a spin system and are less disturbed by noise than other neural networks.

Research by Saccone and the team confirmed that the material was spin glass, evidence that will allow them to describe the properties of the system and how it processes information. AI algorithms developed in spin glass would be “messier” than traditional algorithms, Saccone said, but also more flexible for certain AI applications.

The article: “Direct observation of a dynamic glass transition in a nanomagnetic artificial Hopfield network”, by Michael Saccone, Francesco Caravelli, Kevin Hofhuis, Sergii Parchenko, Yorick A. Birkhölzer, Scott Dhuey, Armin Kleibert, Sebastiaan van Dijken, Cristiano Nisoli & Alan Farhan, in Nature Physics. Link:

Funding: Laboratory-led research and development program at Los Alamos National Laboratory.

‘LA-UR-22-22670’ Version 2

Sherry J. Basler