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

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

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

The integration of machine learning techniques into microstructure design and the prediction of material properties has ushered in a transformative era for materials science. By leveraging advanced ...

Advances in machine learning and shape-memory polymers are enabling engineers to design for mechanical performance first and ...

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 ...

The development of next-generation metallic materials is entering a transformative era driven by data-driven methodologies. Traditional trial-and-error ...

In the era of climate crisis, hydrogen vehicles are emerging as an alternative for eco-friendly mobility. However, the fuel cell, known as the "heart of the hydrogen car," still faces limitations of ...

Researchers developed a machine learning model that accurately predicts which polyimide structures will form liquid crystalline phases, speeding up the design of thermally conductive polymers for ...

More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...

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 ...