Carbon-based materials hold enormous potential for building a sustainable future, but material scientists need tools to properly analyze their atomic structure, which determines their functional properties. X-ray photoelectron spectroscopy (XPS) is one of the tools used to do this, but XPS results can be challenging to interpret. Now, researchers at Aalto have developed a machine-learning tool to improve XPS analyses, which they have made freely available as the XPS Prediction Server.