Progress in the experimental and computational methods of work function evaluation of materials: A review

The work function, which determines the behaviour of electrons in a material, remains a crucial factor in surface science to understand the corrosion rates and interfacial engineering in making photosensitive and electron-emitting devices. The present article reviews the various experimental methods and theoretical models employed for work function measurement along with their merits and demerits are discussed. Reports from the existing methods of work function measurements that Kelvin probe force microscopy (KPFM) is the most suitable measurement technique over other experimental methods. It has been observed from the literature that the computational methods that are capable of predicting the work functions of different metals have a higher computational cost. However, the stabilized Jellium model (SJM) has the potential to predict the work function of transition metals, simple metals, rare-earth metals and inner transition metals. The metallic plasma model (MPM) can predict polycrystalline metals, while the density functional theory (DFT) is a versatile tool for predicting the lowest and highest work function of the material with higher computational cost. The high-throughput density functional theory and machine learning (HTDFTML) tools are suitable for predicting the lowest and highest work functions of extreme material surfaces with cheaper computational cost. The combined Bayesian machine learning and first principle (CBMLFP) is suitable for predicting the lowest and highest work functions of the materials with a very low computational cost. Conclusively, HTDFTML and CBMLFP should be used to explore the work functions and surface energy in complex materials.