If nonliving materials can produce rich, organized mixtures of organic molecules, then the traditional signs we use to ...

A team of researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time -- a development that could lead to the creation of stronger, more ...

(Nanowerk News) Researchers at the University of Toronto’s Faculty of Applied Science & Engineering have used machine learning to design nano-architected materials that have the strength of carbon ...

Imagine having a super-powered lens that uncovers hidden secrets of ultra-thin materials used in our gadgets. Research led by University of Florida engineering professor Megan Butala enables a novel ...

Machine learning interatomic potentials (MLIPs) have become an essential tool to enable long-time scale simulations of materials and molecules at unprecedented accuracies. The aim of this collection ...

Machine learning (ML) enables the accurate and efficient computation of fundamental electronic properties of binary and ternary oxide surfaces, as shown by scientists. Their ML-based model could be ...

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

Two recent developments in artificial intelligence (AI) and machine learning have the potential to accelerate product development by streamlining materials research. Israel-based MaterialsZone, a ...

Engineers are starting to build hardware that does not just run artificial intelligence, it behaves like a primitive form of ...

A recent study published in Small highlights how machine learning (ML) is reshaping the search for sustainable energy materials. Researchers introduced OptiMate, a graph attention network designed to ...

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