Berlin 2024 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 20: Data Driven Material Science: Big Data and Workflows III

MM 20.2: Talk

Tuesday, March 19, 2024, 10:30–10:45, C 243

Thermodynamic and phonon properties of multi-alkali antimonides from density-functional theory and machine learning — •Julia Santana-Andreo, Holger-Dietrich Saßnick, and Caterina Cocchi — Carl von Ossietzky Universität Oldenburg, Institute of Physics

Modern advancements in generating ultrabright electron beams have ushered in innovative experimental techniques in particle accelerators. However, the current challenge lies in improving the quality of electron sources primarily with novel photocathode materials, such as alkali-based semiconductors. In this work, we employ density functional theory combined with machine learning techniques to probe the thermodynamic stability of various alkali-based crystals, emphasizing the role of the approximations taken for the exchange-correlation (xc) functional. Our results reveal that SCAN offers the optimal trade-off between accuracy and computational costs to describe vibrational properties in these materials. Furthermore, it is observed that systems with a higher concentration of Cs atoms exhibit enhanced anharmonicities, which are accurately predicted and characterized with the employed methodology.

Keywords: Phonons; Density-functional-theory; Exchange-correlation functional; Anharmonicity; Multi-alkali antimonides