Surface compositions play a predominant role in the efficiency and lifetime of membranes and catalysts. The surface composition can change during operation due to segregation, thus controlling and predicting the surface composition is essential. Computational modelling can aid in predicting the alloy's stability, along with designing surface alloys and near-surface alloys which can outperform existing catalysts. A computational model to predict surface segregation in ternary alloys is developed. The model is based on Miedema's semi-empirical model that is used to predict mixing enthalpies. The segregation enthalpy is parameterized to describe pairwise interactions between nearest-neighbours and then used in Monte Carlo simulations. Monte Carlo simulations enable to predict short-range ordering in the surface and subsurfaces; both affect the performance of a material as a catalyst. The computational model obtained in this work is able to screen through a vast range of alloy compositions and can qualitatively predict the alloy's stability in a gas environment. In this thesis the model is applied to design a novel ternary Pd-based material for membranes that can be used to separate hydrogen from a gas mixture. Addition of specific amounts of Cu and Zr to Pd results in a material with reduced H2S poisoning as compared to a pure Pd surface as well as an enhanced permeability. The computational model obtained in this work allows to systematically assess the composition of ternary surface alloys and near-surface alloys and is a large improvement over the trial and error approaches currently used.