This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure. Discovering new materials with customizable and optimized properties, driven either by ...

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 ...

A machine learning pipeline identifies low-frequency Raman signatures as reliable indicators of liquid-like ionic conduction ...

Researchers have developed a framework that uses machine learning to accelerate the search for new proton-conducting materials, that could potentially improve the efficiency of hydrogen fuel cells.

Jason Rivas is researching materials at the atomic level to improve reliability and resistance of electronics to space radiation. A PhD student in materials science and engineering at the University ...

The proton-conducting layer currently found in solid oxide fuel cells is typically made from a perovskite structure (left). Using machine learning, a research team, led by Kyushu University, has ...

How can artificial intelligence (AI) machine learning models be used to identify new materials? This is what a recent study published in Nature hopes to address as a team of researchers investigated ...

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 ...

A team of researchers at the Technical University of Munich and ́Ecole Polytechnique Fédérale de Lausanne has developed an innovative computational approach combining machine learning and Raman ...

Machine learning simplifies industrial laser processes Date: May 27, 2025 Source: Swiss Federal Laboratories for Materials Science and Technology (EMPA) Summary: Laser-based metal processing enables ...