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 ...

Machine learning (ML) enables the accurate and efficient computation of fundamental electronic properties of binary and ternary oxide surfaces, as shown by scientists. Their ML-based model could be ...

A new computational method allows modern atomic models to learn from experimental thermodynamic data, according to a ...

AI models are transforming catalyst discovery by combining databases with machine learning and language models, enabling ...

Superconductors sit at the heart of some of the most ambitious technologies on the horizon, from lossless power grids to practical quantum computers, yet finding new ones has long been a slow, ...

Literature searches, simulations, and practical experiments have been part of the materials science toolkit for decades, but the last few years have seen an explosion of machine learning-driven ...

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

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