Published in arXiv, 2025
Discrete diffusion model for generating materials as descriptions of their symmetry properties
Recommended citation: Ekström Kelvinius, F., Andersson, O. B., Parackal, A. S., Qian, D., Armiento, R., & Lindsten, F. (2025). WyckoffDiff-A Generative Diffusion Model for Crystal Symmetry. arXiv preprint arXiv:2502.06485.
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Published in arXiv, 2025
Sequential Monte Carlo method for solving linear-Gaussian inverse problems with diffusion priors
Recommended citation: Ekström Kelvinius, F., Zhao, Z., & Lindsten, F. (2025). Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo. arXiv preprint arXiv:2502.06379.
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Published in The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Three versions of discriminator guidance for autoregressive (diffusion) models
Recommended citation: 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.
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Published in Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS 2023), 2023
Using knowledge distillation for improving performance of molecular graph neural networks
Recommended citation: 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.
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Published in Physical Review Materials, 2022
Evaluating the performance of a GNN for crystals in a high-throughput search setting, and proposing an ML-assisted workflow
Recommended citation: 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.
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Published in ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling, 2023
Investigating the generation order in autoregressive (diffusion) models for graph generation
Recommended citation: Kelvinius, F. E., & Lindsten, F. (2023). Autoregressive Diffusion Models with non-Uniform Generation Order. In ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling.
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