The segregation of solute elements to defects in metals plays a fundamental role for microstructure evolution and the materials performance. However, the available computational data is scattered and inconsistent due to the use of different simulation parameters and methods. We present a high throughput study on grain boundary and surface segregation together with their effect on grain boundary embrittlement using a consistent first‐principles methodology. The data is evaluated for most technologically relevant metals including Al, Cu, Fe, Mg, Mo, Nb, Ni, Ta, Ti, W with the majority of the elements from the periodic table treated as segregating elements. Trends amongst the solute elements are analysed and explained in terms of phenomenological models and the computed data is compared to available literature data. The computed first‐principles data is used for a machine learning investigation showing the capabilities for extrapolation from first‐principles calculation to the whole periodic table of solutes. The present work allows for comprehensive screening of new alloys with improved interface properties.This article is protected by copyright. All rights reserved.