POSTECH researchers demonstrate a foundation model for automating analog circuit layout design.

For years, scientists have worked to uncover how the brain responds to mechanical forces and electromagnetic waves. Computer models offer useful simulations, but they don’t fully capture what goes on inside a living brain. Now, the team from Mizzou’s College of Engineering is working to close that gap by developing 3D-printed models of an artificial human brain.

A research team led by Dr. Byungju, Lee at the Computational Science Research Center of the Korea Institute of Science and Technology (KIST, President Sang-Rok Oh) has identified key factors governing lithium ion movement in amorphous solid electrolytes through AI-based atomic simulations. The team analyzed lithium-ion movement by distinguishing it into 'ease of movement between sites' and 'connectivity of movement paths'. They confirmed that overall performance is more significantly influenced by the difficulty of ions moving from one site to the next than by path connectivity.

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective at capturing relationships between nodes and edges in data, but often overlook higher-order, complex connections. To address this challenge, a research team at The Hong Kong Polytechnic University (PolyU) has developed a new heterogeneous graph attention network, revolutionising the modelling of complex relationships in graph-structured data. This innovation is poised to break through AI application limitations in fields such as neuroscience, logistics, computer vision and biology.

Scientists have created a new type of material that could enable common electronic devices to work faster and use less energy, a study suggests.