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 ...

Generative AI models have been used to create enormous libraries of theoretical materials that could help solve all kinds of ...

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 ...

Overview: AI is transforming materials science by dramatically reducing the time needed to discover and test new materials.Machine learning models analyze massi ...

(a) A feasible route for developing large materials models capable of describing the structure-property relationship of materials. The universal materials model of DeepH accepts an arbitrary material ...

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 ...

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

Topological defects govern how many advanced materials behave, but predicting them has traditionally required slow, resource-intensive simulations. Researchers at Chungnam National University have ...