Machine-learning interatomic potentials (MLIPs) promise near ab initio accuracy at a fraction of the computational cost, unlocking atomistic simulations at scales previously inaccessible. Yet, their accuracy and generalizability remain critically limited ...

Interactive map of 3 million Bluesky users, visualised by their follower pattern.

An academic position as a Postdoctoral Research Associate in AI/Ml-Driven Chemical Discovery - Grade 7 is being advertised on jobs.ac.uk. Click now to find more details and explore additional academic...

Experts from academia and industry offer perspectives on the future direction of autonomous science and knowledge discovery.

We adapted an existing approach to identifying stabilisable crystal structures from prediction sets - the Generalised Convex Hull (GCH) - to improve its application to molecular crystal structures. Th...

Instead of scientists in white coats, it is robots that move between the benches in a University of Liverpool cheminstry laboratory.

Model for predicting molecular crystal properties is readily adaptable to specific tasks, even with limited data

The physical and chemical properties of molecular crystals are a combined function of molecular structure and the molecular crystal packing. Specific crystal packings can enable applications such as p...

Model for predicting molecular crystal properties is readily adaptable to specific tasks, even with limited data

Model for predicting molecular crystal properties is readily adaptable to specific tasks, even with limited data

Amorphous materials are ubiquitous in nature and are widely used for many industrial applications, including catalysis, energy storage, and environmental science. However, significant challenges remain in designing and optimizing amorphous high-entropy materials because of the lack of well-defined structure–activity relationships. Here, we use synthesis systems to discover and optimize amorphous high-entropy oxyhydroxide electrocatalysts within the entire design space for the alkaline oxygen evolution reaction. Amorphous high-entropy electrocatalysts are derived from ultrathin 2D coordination polymers composed of six nonprecious metal elements that were selected from top 16 candidate metal elements involved in oxygen evolution reaction (OER)-related literature searching, which can then be transformed in situ into amorphous oxyhydroxides. Leveraging machine learning (ML) techniques, we establish a composition–activity relationship and thereby identify an optimal composition group by traversing the entire design space (over 1,900,000 compositions). Our ML-model is validated by using 100 compositions in the high-activity region and 588 compositions in the low-activity region, which results in excellent recall values of nearly 100%. The predicted optimal amorphous high-entropy electrocatalyst demonstrates an ultralow overpotential of 159 mV at a current density of 10 mA cm–2 for the alkaline OER in a 1 M KOH while exhibiting ultralong durability 10,218 h under a practical current density of 1 A cm–2 in a 6 M KOH. Our work provides a general strategy for the automatic discovery and optimization of amorphous high-entropy oxyhydroxide electrocatalysts and could significantly impact the development of other amorphous high-entropy materials.

Carmine is a red pigment made from dried cochineal, a scale insect that has been a source of brilliant scarlet reds in clothing and art for more than two millennia, with records dating back to 700 BC....

The vastness of chemical space makes discovering new materials challenging. Here, authors propose a generative AI model enabling crystal structures generation from textual descriptions, accelerating m...