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 ...

MLIP calculations successfully identify suitable dopants for a novel photocatalytic material, report researchers from the ...

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 ...

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 ...

Researchers utilized a machine learning-based strategy to explore and optimize the ratio of transition metals within multi-element materials for sodium-ion batteries. The model analyzes various ...

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 ...

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 ...

Advancements in Material Design and Synthesis It feels like every week there’s some new material that’s supposed ...