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

Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy (XAS) is an important technique for this, as it reveals detailed insights ...

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 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 ...

Artificial Intelligence and its related tools, such as machine learning, deep learning, and neural networks, are revolutionizing every field of life. The domain of materials science and engineering is ...

Artificial intelligence is accelerating material discovery and design by automating analysis, guiding experiments, and enabling predictive modeling across spectroscopy, microscopy, and synthesis.

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 ...

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 ...

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 ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of payments. We live in a world where machines can understand speech, recognize faces, and even generate ...