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 ...
Key TakeawaysThe Materials Project is the most-cited resource for materials data and analysis tools in materials science.The ...
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 ...
This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure. Discovering new materials with customizable and optimized properties, driven either by ...
Dhruv Shenai investigates how machine learning and lab automation are transforming materials science at Cambridge ...
Electro- and photocatalytic materials are central to enabling sustainable energy conversion processes such as water splitting, CO2 reduction, oxygen ...
Technologies that underpin modern society, such as smartphones and automobiles, rely on a diverse range of functional ...
The integration of bioinformatics, machine learning and multi-omics has transformed soil science, providing powerful tools to ...
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