Ekström Kelvinius, F. (2025). Deep Learning for the Atomic Scale : Graph Neural Networks and Deep Generative Models with Some Applications to Materials and Molecules (PhD dissertation, Linköping University Electronic Press). https://doi.org/10.3384/9789181181852
Ekström Kelvinius, F., Andersson, O.B., Parackal, A.S., Qian, D., Armiento, R. & Lindsten, F.. (2025). WyckoffDiff – A Generative Diffusion Model for Crystal Symmetry. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:15130-15147 Available from https://proceedings.mlr.press/v267/ekstrom-kelvinius25a.html.
Ekström Kelvinius, F., Zhao, Z. & Lindsten, F.. (2025). Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:15148-15181 Available from https://proceedings.mlr.press/v267/ekstrom-kelvinius25b.html.
Ekström Kelvinius, F. & Lindsten, F.. (2024). Discriminator Guidance for Autoregressive Diffusion Models. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:3403-3411 Available from https://proceedings.mlr.press/v238/ekstrom-kelvinius24a.html.
Ekström Kelvinius, F., Georgiev, D., Toshev, A., & Gasteiger, J. (2024). Accelerating molecular graph neural networks via knowledge distillation. Advances in Neural Information Processing Systems, 36.
Kelvinius, F. E., & Lindsten, F. (2023). Autoregressive Diffusion Models with non-Uniform Generation Order. In ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling.
Ekström Kelvinius, F., Armiento, R., & Lindsten, F. (2022). Graph-based machine learning beyond stable materials and relaxed crystal structures. Physical Review Materials, 6(3), 033801.
Lecture at Linköping University, Liköping, Sweden
Workshop at Uppsala University, Uppsala, Sweden