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

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 ...

Machine learning potential (MLP) training for surface reconstruction analyses. (a) Workflow for MLP training and large-scale configuration space searching. (b-c) Molecular dynamics (MD) simulations at ...

Technologies that underpin modern society, such as smartphones and automobiles, rely on a diverse range of functional ...

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 ...

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 ...

Researchers have used machine learning to design nano-architected materials that have the strength of carbon steel but the lightness of Styrofoam. The team describes how they made nanomaterials with ...

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 ...

Some of the most encouraging results for reaction-enhancing catalysts come from one material in particular: tin (Sn). While Sn's overall utility as a catalyst is well-known, its underlying ...

Synthesis reaction of Calcium superhydride under high pressure reproduced by the machine learning model. Here, the surface of calcium hydride (CaH 2) melts to absorb hydrogen molecules, then ordered ...