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Machine learning for faster discovery of ion-conducting materials Fast ion transport underpins key technologies such as batteries, fuel cells and sensors, but it is difficult to probe experimentally and expensive to model with conventional atomistic simulations. In this collaborative work…

We developed and studied eight new dyes that absorb and emit light in the near-infrared region, which is invisible to the human eye but very useful in medical and imaging applications. By changing one key atom in the dye structure…

Organic solar cells have been at the centre of research attention in recent decades due to their ease of processing and dramatic rise in efficiency. However, the exact mechanism by which these devices absorb sunlight and convert this energy into…

This research reveals that ‘tuning’ friction of surfactant-containing solutions with electricity depends less on the lubricant’s soap-like molecules and more on the sodium ions surrounding them. In a steel-on-steel system, the lubricant molecules naturally form protective, log-shaped ‘rollers’ that keep…

Automated ab initio calculations have emerged as a powerful tool for computational materials science. Automated workflows offer many benefits over traditional manual approaches, including reproducibility, scalability, and useability. This work presents atomate2, a library of over 100 computational materials science workflows.…

Magnetic fields drastically alter the electronic spectrum, producing a fractal structure known as the Hofstadter butterfly. Our paper demonstrates that this modification of the spectrum induces a phase transition from a Mott insulator to a metal. The image illustrates how the butterfly drives the motion of electrons, thereby transforming an insulator into a metal.

Many of nature’s most interesting physical, chemical, and biological processes exist on timescales beyond the reach of atomistic simulations. A common way for practitioners to still sample these processes in simulations is to identify a small set of coordinates that…

This study used reactive molecular dynamics (ReaxFF) and unsupervised clustering to analyse how aluminium and alumina crystallise from the melt. Aluminium atoms crystallise rapidly via a barrierless process, while alumina grows more slowly through a sequential mechanism: oxygen atoms incorporate…

The exceptional performance of ceria (CeO2) in catalysis and energy conversion is fundamentally governed by the presence of defects in the material, particularly oxygen vacancies. The formation of each oxygen vacancy (VO), which carries an effective +2 charge, is assumed…

Zeolites and silica polymorphs are central to numerous industrial and environmental applications, including catalysis, gas separation, and CO₂ capture. As such, accurate and efficient modelling tools are crucial for advancing the rational design and understanding of these materials. The MACE-MP…