Deep learning for the atomic scale - graph neural networks and deep generative models with some applications to materials and molecules
- Författare
- Linköpings universitet Filip Ekström Kelvinius
- (Filip Ekström Kelvinius., Funding: This research was supported by the Excellence Center at Linköping–Lund in Information Technology (ELLIIT), the Swedish Research Council (VR) grant no. 2020-04122, 2024-05011, the Swedish Foundation for Strategic Research (SSF) Grant No. ICA16-0015, the Knut and Alice Wallenberg Foundation (KAW) via the Wallenberg AI, Autonomous Systems, and Software Program (WASP), the Wallenberg Initiative Material Science for Sustainability (WISE) through the joint WASP-WISE project Generative AI models for property to structure materials prediction, and KAW project 2020.0033. Much of the computations were enabled by the Berzelius resource provided by KAW at the National Supercomputer Centre and the Alvis resource provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at Chalmers e-Commons at Chalmers (C3SE) partially funded by the Swedish Research Council through grant agreement no. 2022-06725., Härtill 5 uppsatser, Diss. (sammanfattning) Linköping : Linköpings universitet, 2025)
- Genre
- Avhandlingar, theses
- Språk
- Engelska
| Förlag | År | Ort | Om boken | ISBN |
|---|---|---|---|---|
| Linköping University. Department of Computer and Information Science | 2025 | Sverige, Linköping | xiii, 74 sidor illustrationer | 978-91-8118-184-5 |