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 ...

Key TakeawaysThe Materials Project is the most-cited resource for materials data and analysis tools in materials science.The ...

Dhruv Shenai investigates how machine learning and lab automation are transforming materials science at Cambridge ...

Researchers from China University of Petroleum (East China), in collaboration with international partners, have reported a ...

Electro- and photocatalytic materials are central to enabling sustainable energy conversion processes such as water splitting, CO2 reduction, oxygen ...

Discover how a new machine learning method can help scientists predict which MOF structures are good candidates for advanced ...

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 ...

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 ...