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 ...

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 ...

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

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 ...

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

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 ...

How additive manufacturing advanced the development of functionally graded materials. Why compositionally graded materials present a greater challenge to materials engineers. How computational ...

This schematic illustrates the integrated AI for Materials (AI4M) framework proposed for OLED material design. It starts from (I) Basic Luminescent Materials, focusing on structural and photophysical ...

In materials science, substances are often classified based on defining factors such as their elemental composition or crystalline structure. This classification is crucial for advances in materials ...