In the domain of metamaterials, the push toward automated design has been accelerated by advances in generative machine learning. The advent of deep ...

Machine learning interatomic potentials (MLIPs) have become an essential tool to enable long-time scale simulations of materials and molecules at unprecedented accuracies. The aim of this collection ...

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

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 ...

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

A new set of simple equations can fast-track the search for metal-organic frameworks (MOFs), a Nobel-Prize-winning class of ...

Researchers from China University of Petroleum (East China), in collaboration with international partners, have reported a comprehensive review of artificial intelligence (AI) techniques integrated ...

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 ...

Dhruv Shenai investigates how machine learning and lab automation are transforming materials science at Cambridge ...

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 ...

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 ...