Researchers at Kiel University in Germany have discovered innovative mechanisms and materials that could transform the biologically inspired information processing field. In today's world of artificial intelligence (AI) and big data, computer usage is increasing with every search engine query and AI-generated text. However, the human brain is still significantly more energy-efficient compared to computers, despite developments like autonomous driving that contribute to the overall energy consumption of computers and data centers. To create more powerful and sustainable computer systems inspired by the brain, a team of researchers from Kiel University's Materials Science and Electrical Engineering departments have identified key requirements for suitable hardware. By creating dynamic materials that mimic biological nervous systems, they have opened up the possibility for a new method of information processing in electronic systems.

Prof Dr Hermann Kohlstedt, a nanoelectronics expert and spokesperson for Kiel University's Collaborative Research Centre 1461 Neurotronics, is looking to nature for inspiration in creating new electronic components and computer architectures. Unlike traditional chips, transistors, and processors, these components would function similarly to the ever-changing network of neurons and synapses in our brains. While supercomputers excel in certain tasks, such as artificial intelligence, they cannot match the ability of humans to handle a variety of everyday tasks, from driving a car to making music to telling stories at social gatherings. However, computers still rely on silicon technology. While there have been advancements in hardware development, networks of neurons and synapses still outperform computers in terms of connectivity and resilience, says materials scientist Dr Alexander Vahl. Further research into new materials and processes is necessary to effectively replicate the dynamic information processing found in biological systems.

To mimic the dynamic behavior of three-dimensional biological nervous systems, the research team focused on developing materials that can change and adapt. They identified seven essential principles that computer hardware must embody to function similarly to the brain. One crucial element is plasticity, which allows for learning and memory processes. While the materials developed by the researchers fulfill many of these principles, there is currently no material that fully embodies all of them.

Prof. Dr. Rainer Adelung, Professor of Functional Nanomaterials, believes that combining materials can lead to new possibilities in computer technology. With the need for more computing power rising, strategies such as miniaturization are no longer sufficient. The research team has developed special granular networks with unique behavior when stimulated by electrical signals using silver-gold nanoparticles. This balance between stability and conductivity mirrors the brain's optimal state known as criticality. In other experiments, zinc oxide nanoparticles and electrochemically formed metal filaments were used to alter network paths via electrical input from oscillators. Coupling these circuits resulted in synchronized signal deflections over time, similar to how electrical impulses exchange information between neurons during conscious sensory perception.

Scientists at Nagoya University in Japan have used supercomputer technology to discover a new method of detecting tiny imperfections called dislocations in polycrystalline materials. These materials are widely used in electronics, solar panels, and other tech devices, but their effectiveness can be hindered by the presence of dislocations.

Polycrystalline materials are a vital component in many devices we use daily, such as smartphones, computers, and cars. However, because of their complex structures, they are challenging to use effectively. Besides their composition, factors like microstructure, dislocations, and impurities can affect the performance of these materials. One significant issue in using polycrystals is the formation of dislocations caused by stress and temperature changes, which can disrupt the arrangement of atoms and affect performance. It is crucial to understand the formation of these dislocations to prevent failures in devices that use polycrystalline materials.

A team of researchers at Nagoya University, led by Professor Noritaka Usami and including Lecturer Tatsuya Yokoi and Associate Professor Hiroaki Kudo, utilized AI to analyze image data of a commonly used material called polycrystalline silicon, which is used in solar panels. The AI created a 3D model in virtual space, allowing the team to identify areas where clusters of dislocations were affecting the material's performance.

The researchers used electron microscopy and theoretical calculations to analyze dislocation clusters and determine how they formed. They found stress distribution in the crystal lattice and staircase-like structures at the boundaries between crystal grains, which contribute to dislocations during crystal growth. This discovery has implications not only for practical applications but also for the study of crystal growth and deformation. The Haasen-Alexander-Sumino (HAS) model is commonly used to understand dislocation behavior in materials, but the researchers believe that their work uncovered previously unrecognized types of dislocations not accounted for by the HAS model.

Furthermore, the team made another surprising discovery while examining the atomic arrangement of these structures. They found significant tensile bond strains along the edges of the staircase-like formations which triggered the generation of dislocations. Usami, one of the experts on this subject, stated that they were amazed and delighted to finally have evidence of dislocations in these structures. This suggests that by controlling the direction in which boundaries spread, we can also control the formation of dislocation clusters. Through a combination of experiments, theory, and AI, they were able to analyze nanoscale regions in polycrystalline materials and shed light on previously unexplained phenomena. This breakthrough research has paved the way for universal guidelines in creating high-performance materials, with potential impacts beyond solar cells to various fields such as ceramics and semiconductors. Improved performance in polycrystalline materials could have a revolutionary effect as they are widely used in society.