Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy (XAS) is an important technique for this, as it reveals detailed insights ...

Testing how quickly a biodegradable plastic actually breaks down in the environment can take months, sometimes years, of lab ...

Researchers developed a machine learning model that accurately predicts which polyimide structures will form liquid crystalline phases, speeding up the design of thermally conductive polymers for ...

Mechanical engineering has traditionally relied on physics, mathematics, and empirical knowledge to design and optimize systems. Machine learning (ML) introduces powerful tools that can complement ...

Imagine a material that can cling firmly to wet surfaces, repair itself after being damaged and remain flexible under ...

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

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 ...

Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...

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