Over the course of the year, we've had the pleasure of working with many talented researchers from across the globe. As 2025 draws to a close, we take a look back at some of the excellent blog posts from our contributors. This work contributes to the field of explainable AI by developing a novel neural network that can be directly transformed into logic. The authors explore the tensions between creators and AI-generated content through a survey of 459 artists. Find out more about work presented at ECAI on generating a comprehensive biomedical knowledge graph question answering dataset.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we attend AIES and ECAI, learn about policy design for two-sided platforms, discover how to balance speed and physical laws in atomic-scale simulations, and find out more about machine learning for chip design. October has been a busy month on the conference front. Over in Madrid, researchers gathered for the conference on Artificial Intelligence, Ethics, and Society (AIES) . The event featured two keynote talks, panel discussions and poster sessions.

When we want to understand how matter behaves, the real action happens at the atomic scale. Heating of water, a chemical reaction in a battery, the way proteins fold in our cells, or how a catalyst works to convert carbon dioxide into useful fuels, all of these processes are governed by the motions and interactions of atoms. Atomic-scale simulations give us a way to explore the microscopic behavior of matter, by tracking how atoms move under the laws of quantum mechanics. These simulations have become essential across physics, chemistry, biology, and materials science. They test hypotheses that experiments cannot easily probe and help design new materials before they are synthesized and tested in the lab.