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

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

Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...

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 ...

A key advantage of the approach lies in its ability to jointly address mechanical resilience and electrical functionality. Li’s group found that by ...

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 ...

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 ...

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 ...