Next Gen Material Learns from Its Past

Researchers have developed a material that has memory and changes its behavior based on past experiences.

The shape and conductivity of the columns formed by the magnetic beads in the magnetic field depend on the strength and history of the field Credit Olli Ikkala Aalto University

We are moving towards the revolution of artificial intelligence (AI) which in itself is a breakthrough for technology, but what if the material has its own intelligence? This can lead to progress to another level. Devices that can change their behavior based on past experiences will help machine learning programs and also make AI more efficient.

Aalto University researchers have developed a new material that changes its electrical behavior based on previous experience, effectively giving it a basic form of adaptive memory. Responsive materials are becoming commonplace in a variety of applications, from sunlight-darkening glasses to drug delivery systems. However, existing materials always work the same way every time. Their response to a change does not depend on their history, nor do they adapt based on their past.

The researchers synthesized micrometer-sized magnetic beads that were then excited by a magnetic field. When the magnet is turned on, the beads gather to form columns. The strength of the magnetic field affects the shape of the pillars, which in turn affects how well they conduct electricity. “With this system, we combined the magnetic field stimulus and the electrical response. Interestingly, we found that the electrical conductivity depends on whether we change the magnetic field quickly or slowly. That means that the electrical response depends in the history of the magnetic field. Electricity also behaves differently when the magnetic field increases or decreases. The answer shows bistability, which is an elementary form of memory. The material behaves as if it has a memory of the magnetic field,” Bo explained. Peng, an Academy Research Fellow at Aalto University.

The system’s memory allows it to behave in a way similar to basic learning. Researchers are motivated to work with neurons. Depending on how often they are stimulated, the synapses of a neuron can be harder or easier to activate. This change, known as short-term synaptic plasticity, makes the connection between a pair of neurons stronger or weaker depending on their recent history. The researchers applied this knowledge to their experiment. When they exposed the beads to a fast pulsing magnetic field, the material became better at conducting electricity, while slow pulsing made it less conductive.

“In the future, there may be many materials inspired by the algorithm with life-like properties, although they will not include the full complexity of biological systems. Such materials will be central to the next generation of soft robots and for medical and environmental monitoring,” added Aalto’s Distinguished Professor Olli Ikkala.

Reference: “Magnetic field-driven particle assembly and jamming for bistable memory and response plasticity” by Xianhu Liu, Hongwei Tan, Carlo Rigoni, Teemu Hartikainen, Nazish Asghar, Sebastian van Dijken, James VI Timonen, Bo Peng and Olli Ikkala, 11 November 2022, Advances in Science. DOI: 10.1126/sciadv.adc9394

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