Manipulating light on the nanoscale allows scientists to create specific structural colors that do away with the need for potentially harmful dyes.

Manipulating light on the nanoscale allows scientists to create specific structural colors that do away with the need for potentially harmful dyes.
Hundreds of RNA‐binding proteins and their associated RNAs have been revealed, which enables the large‐scale prediction of RNA–protein interactions using machine learning methods.
Convolutional neural networks provide stronger predictive performances for pharmacological assays compared to traditional machine learning models.
Machine learning is bringing forth the future of secure communication, swiftly identifying single photons that hold the key to quantum tech.
Study finds tactile learning in education helps kids engage multiple senses, leading to a richer and more interactive learning experience.
Machine learning unravels the secrets of the Gaudin model, paving the way for improved quantum technologies and a deeper understanding of quantum behavior.
For fusion reactions to become practical, parameters such as plasma density and shape must be monitored in real time and impending disruptions responded to instantly.
Automated molecule design through machine learning helps scientists identify and synthesize a new polymer electrolyte for lithium-ion batteries.
Researchers take a different approach to machine learning to uncover the physics of optics in composite materials.
An in-memory computing prototype provides a promising solution for edge computing systems to implement continual learning.