Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their ...

Shanghai, August 21, 2025 — Nuclear energy is widely recognized as one of the most promising clean energy sources for the future, but its safe and efficient use depends critically on the development ...

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

Explore how AI is transforming advanced materials design by analyzing microscopy images to create smarter, faster innovation pipelines.

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 ...

Researchers have used machine learning to design nano-architected materials that have the strength of carbon steel but the lightness of Styrofoam. The team describes how they made nanomaterials with ...

Conventional clustering techniques often focus on basic features like crystal structure and elemental composition, neglecting target properties such as band gaps and dielectric constants. A new study ...

Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...