Regensburg 2022 – scientific programme
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MA: Fachverband Magnetismus
MA 11: Computational Magnetism 1
MA 11.2: Talk
Monday, September 5, 2022, 15:15–15:30, H48
AiiDA-UppASD: Automatic workflow engine for high-throughput mag-netic simulations and machine learning — •Qichen Xu1,2,3, Jonathan Chico4, Manuel Pereiro2, Danny Thonig5, Erik Sjöqvist2, Olle Eriksson2, Anders Bergman2, and Anna Delin1,3 — 1KTH Royal Institute of Technology, Stockholm,Sweden — 2Uppsala University, Uppsala, Sweden — 3Swedish e-Science Research Center, Stockholm,Sweden — 4Sandvik Coromant, Stockholm, Sweden — 5Örebro University,Örebro,Sweden
The ever-raising theoretical peak performance and accessibility of supercomputer resources bring automated simulations to a more important position for studies of magnetic materials properties. Recently, Huber et al. developed the AiiDA framework. In order to perform high-throughput atomic spin dynamics (ASD) simulations and take advantage of AiiDA platform and its build-in DFT plugins, we introduce the AiiDA-UppASD plugin which allows users to access to the majority of the functionalities of the UppASD code within the AiiDA framework via a Python package. In addition, several robust built-in workflows are also provided for managing ASD simulations, handling possible errors, and providing common modular workchains. With these workflows, complex tasks like high-throughput simulations of magnetodynamic properties as well as the determination of spin-wave excitation spectra and magnetic phase diagrams can be performed in a very efficient manner. Meanwhile, a machine learning (ML) prepared data generation workflow is also designed in order to offer ASD-related databases for the ML community to benchmark and develop methods.