SKM 2023 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
TT: Fachverband Tiefe Temperaturen
TT 56: Focus Session: Making Experimental Data F.A.I.R. – New Concepts for Research Data Management I (joint session O/TT)
TT 56.4: Vortrag
Donnerstag, 30. März 2023, 16:00–16:15, WIL A317
FAIR Data Infrastructure for Computation: Advanced many-body methods. — •José M. Pizarro1, Nathan Daelman1, Joseph F. Rudzinski1, 2, Luca M. Ghiringhelli1, Roser Valentí3, Silvana Botti4, and Claudia Draxl1 — 1Institut für Physik und IRIS-Adlershof, Humboldt-Universität zu Berlin — 2Max-Planck-Institut für Polymer Forschung, Mainz — 3Institut für Theoretische Physik, Goethe University Frankfurt am Main — 4Institut für Festkörpertheorie und Optik, Friedrich-Schiller-Universität Jena
Big-data analyses and machine-learning approaches have recently emerged as a new paradigm to study and predict properties of materials. In order to perform these analyses, materials data have to be structured in a FAIR (findable, accessible, interoperable, and reusable) format [1]. While most of the current databases deal with density-functional-theory (DFT) calculations, there is a clear need for developing FAIR-data schema for methodologies going beyond DFT. Methods such as the GW approximation, dynamical mean-field theory, and time-dependent DFT allow to calculate excited- and many-body-states properties beyond DFT, thus having a direct quantitative comparison with experiments. In this talk, we will introduce the achievements and challenges undertaken within the FAIRmat consortium towards fully structuring the (meta)data of all these techniques. We demonstrate how users can analyze the data and compare with angle-resolved photoemission spectroscopy.
[1] M. Scheffler et al., Nature 604, 635 (2022).