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

What is changing now is the integration of computation, automation and feedback into a single loop. The NIMS team’s high-throughput sensor platform is designed to generate not just isolated ...

Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...

From DFT calculation to ML prediction, the potential catalysts with highly active and selective performance are efficiently screened by four ML models, i.e. decision tree, random forest, support ...

Synthesizer is a platform that combines automated chemical synthesis, high-throughput characterization, and data-driven ...

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

In numerous scientific fields, high-throughput experimentation methods combined with artificial intelligence (AI) show great ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

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 ...

Artificial intelligence is accelerating material discovery and design by automating analysis, guiding experiments, and enabling predictive modeling across spectroscopy, microscopy, and synthesis.

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 ...