Abstract
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 integrated into the hiphive package 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.
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