Organic electrochemical transistor (OECT), a powerful tool for chemical and biological sensing, can operate directly in aqueous environment at low voltages, which makes it ideal for wearable and ...

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 ...

The special issue aims to bring together high-quality research that demonstrates the transformative role of Artificial Intelligence and Machine Learning ...

Researchers at the University of Toronto's Faculty of Applied Science & Engineering have used machine learning to design nano ...

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 ...

Imagine having a super-powered lens that uncovers hidden secrets of ultra-thin materials used in our gadgets. Research led by University of Florida engineering professor Megan Butala enables a novel ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

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 ...

Shanghai, August 21, 2025 — Nuclear energy is widely recognized as one of the most promising clean energy sources for the future, but its safe and efficient use depends critically on the development ...

AI could transform materials R&D. But how it does this, and how well it is adopted, is yet to be seen. Here, we take a look ...