This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure. Discovering new materials with customizable and optimized properties, driven either by ...
A recent study published in Small highlights how machine learning (ML) is reshaping the search for sustainable energy materials. Researchers introduced OptiMate, a graph attention network designed to ...
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 ...
X-ray absorption spectroscopy (XAS) provides valuable information about a material’s properties and electronic states. However, it requires extensive expertise and manual effort for conventional ...
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 ...
Two recent developments in artificial intelligence (AI) and machine learning have the potential to accelerate product development by streamlining materials research. Israel-based MaterialsZone, a ...
Senvol is to demonstrate that its machine learning software can accurately predict the material performance of parts made on ...
Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...
In recent years, JupyterLab has rapidly become the tool of choice for data scientists, machine learning (ML) practitioners, and analysts worldwide. This powerful, web-based integrated development ...
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