Machine learning interatomic potentials (MLIPs) have become an essential tool to enable long-time scale simulations of materials and molecules at unprecedented accuracies. The aim of this collection ...

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 ...

Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed ...

If nonliving materials can produce rich, organized mixtures of organic molecules, then the traditional signs we use to ...

A joint research team from NIMS, Tokyo University of Science, and Kobe University has developed a new artificial intelligence ...

Found in knee replacements and bone plates, aircraft components, and catalytic converters, the exceptionally strong metals known as multiple principal element alloys (MPEA) are about to get even ...

Shanghai, August 21, 2025 — Nuclear energy is widely recognized as one of the most promising clean energy sources for the future, but its safe and efficient use depends critically on the development ...

Hydrogen peroxide is widely used but energy-intensive to produce. A new machine-learning framework helps find catalysts that ...

Computational Chemistry is the study of complex chemical problems using a combination of computer simulations, chemistry theory and information science. Also called cheminformatics, this field enables ...

In recent years, power consumption by machine learning technologies, represented by deep learning and generative artificial ...