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 ...

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

When engineers at Sumitomo Riko needed to speed up the design cycle for automotive rubber and polymer components, they turned ...

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 ...

A team of researchers has used machine learning to design a new stainless steel alloy built specifically for 3D printing, one ...

As AI's integration in the process of designing and improving industrial infrastructure progresses, governance needs to ...

A machine-learning strategy has generated a new class of ultra-high strength and ductility steel for 3D printing that costs ...

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 ...

Mechanical engineering has traditionally relied on physics, mathematics, and empirical knowledge to design and optimize systems. Machine learning (ML) introduces powerful tools that can complement ...

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 ...