BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250822T115805Z
LOCATION:Room 5.0A52
DTSTART;TZID=Europe/Stockholm:20250616T122000
DTEND;TZID=Europe/Stockholm:20250616T125000
UID:submissions.pasc-conference.org_PASC25_sess144_msa137@linklings.com
SUMMARY:Reproducible High-Throughput Computational Design for Sustainable 
 Materials: A Focus on Photocathodes and Beyond
DESCRIPTION:Holger-Dietrich Saßnick and Caterina Cocchi (Institute of Phys
 ics, Carl-von-Ossietzky Universität Oldenburg)\n\nCesium-telluride photoca
 thodes are established materials for electron sources in particle accelera
 tors. While ab initio methods like density functional theory (DFT) show gr
 eat potential to complement experimental research efforts [Cocchi & Saßnic
 k, Micromachines 12, 1002 (2021)], their performance is hindered by the po
 or control of the microstructure and stoichiometry during growth. To overc
 ome these limitations, computational predictions and high-throughput scree
 ning are essential to identify and characterize these systems. This applic
 ation stimulated the development of aim2dat (https://aim2dat.github.io/), 
 a numerical library implementing workflows to perform DFT calculations ens
 uring data provenance and reproducibility, in addition to an effective and
  sustainable usage of high-performance computing resources. In the first s
 tep, the stability and electronic properties of a set of Cs-Te crystal str
 uctures and stoichiometries are analyzed [Saßnick & Cocchi, J. Chem. Phys.
  156, 104108 (2022)]. Next, surface slabs of the Cs2Te compound are comput
 ed and their electronic properties are discussed [Saßnick & Cocchi, NPJ Co
 mput. Mater. 10, 38 (2024)]. Finally, to expand the pool of crystals beyon
 d the experimentally resolved systems, machine learning models are incorpo
 rated to predicting new binary stable cesium-telluride crystals [Saßnick &
  Cocchi, Adv. Theory Simul., 2401344 (2025)]. The proposed approach aims t
 o accelerate the discovery and optimization of high-performance Cs-Te phot
 ocathodes.\n\nDomain: Chemistry and Materials, Climate, Weather, and Earth
  Sciences, Physics\n\nSession Chairs: Nataliya Paulish (Paul Scherrer Inst
 itute); Carlo Pignedoli (Swiss Federal Laboratories for Materials Science 
 and Technology, Empa; National Centre for Computational Design and Discove
 ry of Novel Materials (MARVEL), Switzerland); and Giovanni Pizzi (Paul Sch
 errer Institute)\n\n
END:VEVENT
END:VCALENDAR
