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 ...

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 ...

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 ...

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 ...

An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new ...