Research authored by partners from the Bottle Consortium and published in Nature Communications this month aims to challenge ...

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...

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 ...

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has ...

The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack of a ...

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 ...

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 ...

Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their ...

Muons tend to scatter more from high-atomic-number materials, so the technique is particularly sensitive to the presence of materials such as uranium. As a result, it has been used to create systems ...

The importance of digital tools and simulation for successful composite parts design is well established, whether for aircraft wings, automotive bumper beams or bicycle frames. Over the past decade, ...