This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure. Discovering new materials with customizable and optimized properties, driven either by ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy ...

Superconductors sit at the heart of some of the most ambitious technologies on the horizon, from lossless power grids to ...

A study in Nature Communications by Michele Ceriotti’s group at EPFL has introduced a new dataset and model that greatly improve the efficiency of machine-learning interatomic potentials (MLIPs) and ...

Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development.

Hydrogen peroxide is widely used but energy-intensive to produce. A new machine-learning framework helps find catalysts that ...

Overview of class Key models of electron and ion vacancy transport in hard and soft, crystalline and non-crystalline materials, including hopping, tunneling, polaronic transport and mixed conduction.

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

Learn Your Way is now available in Google Labs. Google's education-centered AI models personalize material. AI companies continue to market study tools to younger users. Google is introducing a new ...