Minisymposium Presentation

Cesium-telluride photocathodes are established materials for electron sources in particle accelerators. While ab initio methods like density functional theory (DFT) show great potential to complement experimental research efforts [Cocchi & Saßnick, Micromachines 12, 1002 (2021)], their performance is hindered by the poor control of the microstructure and stoichiometry during growth. To overcome these limitations, computational predictions and high-throughput screening are essential to identify and characterize these systems. This application stimulated the development of aim2dat (https://aim2dat.github.io/), a numerical library implementing workflows to perform DFT calculations ensuring data provenance and reproducibility, in addition to an effective and sustainable usage of high-performance computing resources. In the first step, the stability and electronic properties of a set of Cs-Te crystal structures and stoichiometries are analyzed [Saßnick & Cocchi, J. Chem. Phys. 156, 104108 (2022)]. Next, surface slabs of the Cs2Te compound are computed and their electronic properties are discussed [Saßnick & Cocchi, NPJ Comput. Mater. 10, 38 (2024)]. Finally, to expand the pool of crystals beyond the experimentally resolved systems, machine learning models are incorporated to predicting new binary stable cesium-telluride crystals [Saßnick & Cocchi, Adv. Theory Simul., 2401344 (2025)]. The proposed approach aims to accelerate the discovery and optimization of high-performance Cs-Te photocathodes.