SpinView: General Interactive Visual Analysis Tool for Multiscale Computational Magnetism

POSTER

Abstract

Multiscale magnetic simulations, including micromagnetic and atomistic spin dynamics simulations, are widely used in the study of complex magnetic systems over a wide range of spatial and temporal scales. The advances in these simulation technologies have generated considerable amounts of data. However, a versatile and general tool for visualization, filtering, and denoising this data is largely lacking. To overcome these limitations, we have developed SpinView, a general interactive visual analysis tool for graphical exploration and data distillation. Combined with dynamic filters and a built-in database, it is possible to generate reproducible publication-quality images, videos, or portable interactive webpages within seconds. Since the basic input to SpinView is a vector field, it can be directly integrated with any spin dynamics simulation tool. With minimal effort on the part of the user, SpinView delivers a simplified workflow, speeds up analysis of complex datasets and trajectories, and enables new types of analysis and insight. SpinView is available from https://mxjk851.github.io/SpinView/.

* This work was financially supported by the Knut and Alice Wallenberg Foundation through grant numbers 2018.0060, 2021.0246, and 2022.0108.Q.X. acknowledges the China Scholarship Council (201906920083). O.E. and A.D. acknowledge support from the Wallenberg Initiative Materials Science for Sustainability (WISE) funded by the Knut and Alice Wallenberg Foundation (KAW). AD also acknowledges financial support from the Swedish Research Council (Vetenskapsrådet, VR), Grant No. 2016-05980 and Grant No. 2019-05304. O.E. also acknowledges support by the Swedish Research Council (VR), the European Research Council (854843-FASTCORR), eSSENCE and STandUP. The computations data handling were partly enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputing Centre (NSC, Tetralith cluster) partially funded by the Swedish Research Council through grant agreement no.2018-05973 and by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at the National Supercomputing Centre (NSC, Tetralith cluster) partially funded by the Swedish Research Council through grant agreement no.2022-06725.

Publication: https://arxiv.org/abs/2309.17367

Presenters

  • Qichen Xu

    KTH Royal Institute of Technology

Authors

  • Qichen Xu

    KTH Royal Institute of Technology

  • Olle Eriksson

    Uppsala University

  • Anna Delin

    KTH Royal Institute of Technology