SKM 2023 – wissenschaftliches Programm

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O: Fachverband Oberflächenphysik

O 9: Focus Session: Frontiers of Electronic-Structure Theory I (joint session O/HL)

Montag, 27. März 2023, 10:30–13:00, TRE Ma

Electronic-structure calculations, based on density-functional theory (DFT) and methodology beyond, are getting increasingly involved as they face the following challenges: First, investigations of modern materials typically require large unit cells, owing to complex crystal structures, mixed compositions, internal interfaces, etc. Second, at the same time, they often require advanced methods, including hybrid functionals of DFT, Green-function techniques from many-body perturbation theory (MBPT), high-level wavefunction-based methods like coupled-cluster (CC) theory, or quantum Monte-Carlo simulations. All these methods should ideally be implemented in scientific software that is running efficiently on modern supercomputers. With both methodology and computer architectures exhibiting increasing complexity, collaborative development and shared tools, including ready-to-use libraries and codes, are becoming indispensable. This interdisciplinary symposium covers recent progress in the broad area of electron-structure methods and highly-sophisticated tools that enable the entire community to explore most exciting materials from different perspectives to either predict peculiar features or get insight into measured counterparts.

Organizers: Claudia Draxl (HU Berlin), Dorothea Golze (TU Dresden), Xavier Gonze (U Louvain), and Andris Gulans (U Latvia)

	10:30	O 9.1	Testing the hell out of DFT codes with virtual oxides — Emanuele Bosoni, •Stefaan Cottenier, and Giovanni Pizzi
	10:45	O 9.2	High-throughput absorption spectra obtained by beyond-DFT workflows — •Fabian Peschel, Alexander Buccheri, and Claudia Draxl
	11:00	O 9.3	Topical Talk: Large-scale machine-learning assisted discovery and characterization of materials — •Miguel Alexandre Lopes Marques
	11:30		15 min. break
	11:45	O 9.4	Predicting the electronic structure at any length scale with machine learning — •Attila Cangi
	12:00	O 9.5	Demonstrating temperature transferability of neural network models replacing modern density functional theory — •Lenz Fiedler and Attila Cangi
	12:15	O 9.6	Pure non-local machine-learned density functional theory for electron correlation — •Johannes T. Margraf
	12:30	O 9.7	Predicting the response of the electron density to electric field using machine learning — •Alan Lewis and Mariana Rossi
	12:45	O 9.8	Analysis of Batching Methods in Graph Neural Network Models for Materials Science — •Daniel Speckhard, Tim Bechtel, Jonathan Godwin, and Claudia Draxl