Glowing gunshot residue: New method illuminates crime scene clues Crime scene investigation may soon become significantly more accurate and efficient thanks to a new method for detecting gunshot residues. Researchers from the groups of Wim Noorduin (AMOLF/University of Amsterdam) and Arian van Asten (University of Amsterdam) developed the technique that converts lead particles found in gunshot residue into light-emitting semiconductors. This method is faster, more sensitive, and easier to use than current alternatives.
https://phys.org/news/2025-04-....gunshot-residue-meth

AI uses extrapolative learning to master materials prediction beyond existing data A research group has developed an innovative machine learning technology that enables predictions beyond the distribution of training data and demonstrated its effectiveness in materials research. The team includes Kohei Noda, a researcher at JSR Corporation, and Professor Ryo Yoshida at the Institute of Statistical Mathematics.
https://phys.org/news/2025-04-....ai-extrapolative-mas

Crystallography-informed AI achieves high performance in predicting novel crystal structures A research team from the Institute of Statistical Mathematics and Panasonic Holdings Corporation has developed a machine learning algorithm, ShotgunCSP, that enables fast and accurate prediction of crystal structures from material compositions. The algorithm achieved world-leading performance in crystal structure prediction benchmarks.
https://phys.org/news/2025-04-....crystallography-ai-h

Engineers develop eco-friendly plastic from mineral found in seashells According to UNESCO, plastic waste makes up 80% of all marine pollution, with 8–10 million metric tons of plastic making its way into our oceans each year. USC Viterbi School of Engineering researchers have discovered a mineral commonly found in seashells could be the key to a safer plastic alternative.
https://phys.org/news/2025-04-....eco-friendly-plastic

AI enhances molecular design with uncertainty quantification In a major step toward more reliable AI-assisted molecular design, researchers from National Taiwan University have demonstrated that incorporating uncertainty quantification (UQ) into graph neural network (GNN) models significantly improves both the efficiency and robustness of molecular optimization.
https://phys.org/news/2025-04-....ai-molecular-uncerta