Physicists use machine learning to find out how layered gases and metals melt
Peer-Reviewed Publication
This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.
Updates every hour. Last Updated: 4-Nov-2025 20:11 ET (5-Nov-2025 01:11 GMT/UTC)
As lithium-ion batteries (LIBs) continue to power electric vehicles and energy storage systems, their long-term health remains a critical challenge. A groundbreaking new method combines deep learning with physical modeling to deliver rapid, accurate degradation diagnosis at the electrode level. This innovative approach requires only 11 data points from a charging cycle, achieving reliable predictions in just 2.5 minutes. By simplifying the data needed and eliminating the need for specialized equipment, this technique offers a faster, more accessible solution to battery health monitoring—paving the way for safer, more efficient battery management in large-scale applications.
This study introduces a deep-learning system for rapid, automated detection and classification of tiny calcium deposits (microcalcifications) in mammograms to aid early breast cancer diagnosis. Leveraging a multi-center dataset of 4,810 biopsy-confirmed mammograms, our pipeline uses a Faster RCNN model with a feature-pyramid backbone to detect and classify microcalcifications—the pipeline requires no hand-tuned rules and provides both the overall cancer risk and highlighted lesion regions in seconds per image. On unseen test data, it achieved overall classification accuracy of 72% for discriminating between benign and malignant breasts and 78% sensitivity of malignant breast cancer prediction, marking a significant step toward AI-assisted, cost-effective breast-cancer screening that can run on standard radiology workstations.