update bibliography

bib/bibliography.bib

@@ -10796,6 +10796,17 @@ Subject\_term\_id: computational-methods;density-functional-theory;method-develo
   file = {/Users/wasmer/Nextcloud/Zotero/Kolb et al_2017_Discovering charge density functionals and structure-property relationships.pdf}
 }
+@artwork{kolja21FileEuropeanUnion2020,
+  title = {File:{{European Union}} Main Map.Svg},
+  shorttitle = {European {{Union}} Main Map},
+  author = {{Kolja21}},
+  date = {2020-02-01},
+  url = {https://commons.wikimedia.org/wiki/File:European_Union_main_map.svg},
+  urldate = {2025-02-04},
+  keywords = {/unread,CC license,European Union,for introductions,graphics,maps,resources,Wikimedia Commons},
+  file = {/Users/wasmer/Zotero/storage/YFMF2F7I/FileEuropean_Union_main_map.html}
+}
+
 @article{kongDensityStatesPrediction2022,
   title = {Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings},
   author = {Kong, Shufeng and Ricci, Francesco and Guevarra, Dan and Neaton, Jeffrey B. and Gomes, Carla P. and Gregoire, John M.},
@@ -15881,6 +15892,24 @@ Subject\_term\_id: magnetic-properties-and-materials},
   file = {/Users/wasmer/Nextcloud/Zotero/Pozdnyakov_Ceriotti_2024_Smooth, exact rotational symmetrization for deep learning on point clouds.pdf;/Users/wasmer/Zotero/storage/X8KQDF53/2305.html}
 }
+@article{priceAIAcceleratedElectronicMaterials2025,
+  title = {{{AI-Accelerated Electronic Materials Discovery}} and {{Development}}},
+  author = {Price, Christopher and Takeuchi, Ichiro and Rondinelli, James and Chen, Wei and Hinkle, Christopher},
+  date = {2025-02},
+  journaltitle = {Computer},
+  volume = {58},
+  number = {2},
+  pages = {98--104},
+  issn = {1558-0814},
+  doi = {10.1109/MC.2024.3515392},
+  url = {https://ieeexplore.ieee.org/document/10857827},
+  urldate = {2025-02-01},
+  abstract = {Recent advances in AI applied to high-throughput materials discovery, synthesis, and processing offer a pathway to accelerated breakthroughs and scaled optimization of advanced electronic materials for data-intensive computation.},
+  eventtitle = {Computer},
+  keywords = {/unread,AML,Automation,Bayesian optimization,experimental science,Gaussian process,High-throughput,materials acceleration platforms,materials characterization,materials processing,ML,MLP,nanomaterials,review,review-of-AI4science,review-of-AML},
+  file = {/Users/wasmer/Nextcloud/Zotero/Price et al. - 2025 - AI-Accelerated Electronic Materials Discovery and Development.pdf;/Users/wasmer/Zotero/storage/BKTL8947/10857827.html}
+}
+
 @book{princeUnderstandingDeepLearning2023,
   title = {Understanding Deep Learning},
   author = {Prince, Simon J. D.},
@@ -19453,6 +19482,26 @@ Subject\_term\_id: electronic-devices;electronic-properties-and-materials;ferroe
   file = {/Users/wasmer/Nextcloud/Zotero/Uhrin et al. - 2024 - Machine learning Hubbard parameters with equivariant neural networks.pdf;/Users/wasmer/Zotero/storage/L7KIG7MJ/2406.html}
 }
+@article{uhrinMachineLearningHubbard2025,
+  title = {Machine Learning {{Hubbard}} Parameters with Equivariant Neural Networks},
+  author = {Uhrin, Martin and Zadoks, Austin and Binci, Luca and Marzari, Nicola and Timrov, Iurii},
+  date = {2025-01-25},
+  journaltitle = {npj Computational Materials},
+  shortjournal = {npj Comput Mater},
+  volume = {11},
+  number = {1},
+  pages = {1--10},
+  publisher = {Nature Publishing Group},
+  issn = {2057-3960},
+  doi = {10.1038/s41524-024-01501-5},
+  url = {https://www.nature.com/articles/s41524-024-01501-5},
+  urldate = {2025-01-30},
+  abstract = {Density-functional theory with extended Hubbard functionals (DFT\,+\,U\,+\,V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations. Here, we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors, directly capturing the electronic structure, local chemical environment, and oxidation states of the system at hand. We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations, as implemented in density-functional perturbation theory (DFPT), and structural relaxations. Remarkably, when trained on data from 12 materials spanning various crystal structures and compositions, our model achieves mean absolute relative errors of 3\% and 5\% for Hubbard U and V parameters, respectively. By circumventing computationally expensive DFT or DFPT self-consistent protocols, our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead, while approaching the accuracy of DFPT. Moreover, owing to its robust transferability, the model facilitates accelerated materials discovery and design via high-throughput calculations, with relevance for various technological applications.},
+  langid = {english},
+  keywords = {/unread,Computational methods,Electronic properties and materials,Theoretical chemistry},
+  file = {/Users/wasmer/Nextcloud/Zotero/Uhrin et al. - 2025 - Machine learning Hubbard parameters with equivariant neural networks.pdf}
+}
+
 @article{uhrinWorkflowsAiiDAEngineering2021,
   title = {Workflows in {{AiiDA}}: {{Engineering}} a High-Throughput, Event-Based Engine for Robust and Modular Computational Workflows},
   shorttitle = {Workflows in {{AiiDA}}},