  Modern advances in generating ultrabright electron beams have unlocked
unprecedented experimental advances based on synchrotron radiation. Current
challenges lie in improving the quality of electron sources with novel
photocathode materials such as alkali-based semiconductors. To unleash their
potential, a detailed characterization and prediction of their fundamental
properties is essential. In this work, we employ density functional theory
combined with machine learning techniques to probe the thermodynamic stability
of various alkali antimonide crystals, emphasizing the role of the
approximations taken for the exchange-correlation potential. Our results reveal
that the SCAN functional offers an optimal trade-off between accuracy and
computational costs to describe the vibrational properties of these materials.
Furthermore, it is found that systems with a higher concentration of Cs atoms
exhibit enhanced anharmonicities, which are accurately predicted and
characterized with the employed methodology.
