Representing a molecule in a way that captures both its structure and function is central to tasks such as molecular property prediction, drug drug ...

Overview: Interpretability tools make machine learning models more transparent by displaying how each feature influences ...

This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...

Crystal ball: In its first hurricane season, Google's Deepmind AI framework not only matched decades of human expertise but surpassed the output of two of the world's most advanced supercomputer ...

Researchers have developed a hybrid CFD-neural network model for predicting TAIs in hydrogen-fueled turbines, improving ...

A research team shows that phenomic prediction, which integrates full multispectral and thermal information rather than ...

After traumatic brain injury (TBI), some patients may recover completely, while others retain severe disabilities. Accurately evaluating prognosis is challenging in patients on life-sustaining therapy ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...