Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development.

Scientists at the University of Oxford demonstrate an approach to interpreting how materials interact with polarized light, ...

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

A model developed by researchers at MIT can help predict proton movement inside materials based on hydrogen bond length and ...

Hydrogen peroxide is widely used in everyday life, from disinfectants and medical sterilization to environmental cleanup and ...

UL’s Bernal Institute hosts materials scientists collaborating with Kyoto University’s Prof Susumu Kitagawa, winner of the Nobel Prize in Chemistry.

Moon-Ho Jo, director of the IBS Center for Van der Waals Quantum Solids in Korea, tells Physics World how breakthroughs in ...

Discovering new inorganic materials is central to advancing technologies in catalysis, energy storage, semiconductors, and ...

It’s possible that AI could give materials discovery a much-needed jolt and usher in a new age that brings superconductors ...

To speed the introduction of solutions for 3M customers, these tools leverage generative AI, advanced modeling, and simulation-ready data cards, empowering users to design and digitally validate ...

A study in Nature Communications by Michele Ceriotti’s group at EPFL has introduced a new dataset and model that greatly improve the efficiency of machine-learning interatomic potentials (MLIPs) and ...

A number of advanced energy technologies—including fuel cells, electrolyzers, and an emerging class of low-power electronics—use protons as the key charge carrier. Whether or not these devices will be ...