Literature searches, simulations, and practical experiments have been part of the materials science toolkit for decades, but the last few years have seen an explosion of machine learning-driven ...

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

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 ...

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 ...

Designing materials that are both lightweight and exceptionally robust has always been a goal for engineers. However, ...

Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their ...

Quantum calculations of molecular systems often require extraordinary amounts of computing power; these calculations are typically performed on the world’s largest supercomputers to better understand ...

By applying machine learning techniques, engineers at MIT have created a new method for 3D printing metal alloys that produce ...

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 ...

MIT researchers have designed a printable aluminum alloy that’s five times stronger than cast aluminum and holds up at ...

The cover image of 10/2024 issue of Bioconjugate Chemistry, displaying a tunable ligand-protected gold nanocluster as a drug delivery system with high affinity to integrin αvβ3, a key regulator of ...