Dhruv Shenai investigates how machine learning and lab automation are transforming materials science at Cambridge ...

How can artificial intelligence (AI) machine learning models be used to identify new materials? This is what a recent study published in Nature hopes to address as a team of researchers investigated ...

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 ...

Technologies that underpin modern society, such as smartphones and automobiles, rely on a diverse range of functional ...

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 ...

During X-ray diffraction experiments, bright lasers shine on a sample, producing diffracted images that contain important information about the material's structure and properties. But conventional ...

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 ...

Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...

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 ...

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 ...

Artificial Intelligence and its related tools, such as machine learning, deep learning, and neural networks, are revolutionizing every field of life. The domain of materials science and engineering is ...