Carbon dioxide (CO2) is a primary driver of climate change in Earth's atmosphere. At the State University of New York at ...

Chiral 2D metal halide perovskites (MHPs) are among the most promising materials for future technologies that exploit the ...

Climate and ocean models use a series of equations to represent complex natural processes. However, the equations used in ...

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...

New machine learning framework predicts promising nucleoside hydrogels before they are synthesized and tested in the ...

This review explains how soft materials, scalable manufacturing, energy-efficient hardware, and AI are converging to create ...

More than 20 faculty members and several students from across academic disciplines attended a two-day training workshop on June 4–5 to learn how AI machine-learning skills can assist with their ...

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...

The seven companies listed here cover the realistic range of what a buyer will encounter in 2026: embedded ML teams that own ...

Image courtesy by QUE.com As we navigate the landscape of 2026, we find ourselves no longer merely using Machine Learning (ML) but ...

The Alexander von Humboldt Foundation has awarded the Humboldt Research Award to Yuri Mishin (George Mason University, USA) and Sang Ho Oh (Korea Institute of Energy Technology, Republic of Korea). • ...

Synthesizability – the ability to reliably produce a theoretically predicted material – remains one of the least understood aspects of modern materials science. The newly funded ERC project Autonomous ...