Gabriel Gomes built an agent that turns plain English into physical experiments, enabling research that humans alone could never sustain ...

A physics informed machine learning model predicts thermal conductivity from infrared images in milliseconds, enabling fast, ...

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of uncertainty. Researchers have developed a lightweight machine learning framework that ...

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...

A brisk theatrical thriller, “Data” perfectly captures the slick, grandiose language with which tech titans justify their ...

Based on these challenges, a comprehensive reassessment of how AI should be deployed in electrocatalysis has become urgently needed. Addressing this need, a review published (DOI: 10.1016/j.esci.2025.

Popular paperbacks are being translated with the help of machines, raising anxiety among professionals in the field.

Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...

A research team led by Prof. Zhonghua Li from Harbin Institute of Technology has discovered how spin density symmetry breaking in single-atom ...

Spin density symmetry breaking in single-atom catalysts can significantly enhance the performance of hydrogen evolution ...

Artificial intelligence (AI) and machine learning (ML) hold significant promise in advancing the field of toxicology by ...