| Active sites in Cr(III)-based ethylene polymerization catalysts from machine-learning-supported XAS and EPR spectroscopy
Ashuiev, A., Giorgia Nobile, A., Trummer, D., Klose, D., Guda, S., Safonova, O. V., … Jeschke, G. (2024). Active sites in Cr(III)-based ethylene polymerization catalysts from machine-learning-supported XAS and EPR spectroscopy. Angewandte Chemie International Edition, 63(1), e202313348 (7 pp.). https://doi.org/10.1002/anie.202313348 |
| Evidence for tWZ production in proton-proton collisions at √s = 13 TeV in multilepton final states
Hayrapetyan, A., Tumasyan, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., … Zhokin, A. (2024). Evidence for tWZ production in proton-proton collisions at √s = 13 TeV in multilepton final states. Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics, 855, 138815 (25 pp.). https://doi.org/10.1016/j.physletb.2024.138815 |
| Performance analysis of data-driven and physics-informed machine learning methods for thermal-hydraulic processes in Full-scale Emplacement experiment
Hu, G., Prasianakis, N., Churakov, S. V., & Pfingsten, W. (2024). Performance analysis of data-driven and physics-informed machine learning methods for thermal-hydraulic processes in Full-scale Emplacement experiment. Applied Thermal Engineering, 245, 122836 (17 pp.). https://doi.org/10.1016/j.applthermaleng.2024.122836 |
| Deep learning applications in protein crystallography
Matinyan, S., Filipcik, P., & Abrahams, J. P. (2024). Deep learning applications in protein crystallography. Acta Crystallographica Section A: Foundations and Advances, 80(1), 1-17. https://doi.org/10.1107/S2053273323009300 |
| Cements and concretes materials characterisation using machine-learning-based reconstruction and 3D quantitative mineralogy via X-ray microscopy
Mitchell, R. L., Holwell, A., Torelli, G., Provis, J., Selvaranjan, K., Geddes, D., … Kearney, S. (2024). Cements and concretes materials characterisation using machine-learning-based reconstruction and 3D quantitative mineralogy via X-ray microscopy. Journal of Microscopy, 29, 137-145. https://doi.org/10.1111/jmi.13278 |
| From outdoor to indoor air pollution source apportionment: answers to ten challenging questions
Saraga, D., Duarte, R. M. B. O., Manousakas, M. I., Maggos, T., Tobler, A., & Querol, X. (2024). From outdoor to indoor air pollution source apportionment: answers to ten challenging questions. Trends in Analytical Chemistry, 178, 117821 (8 pp.). https://doi.org/10.1016/j.trac.2024.117821 |
| Exploring crystallographic texture manipulation in stainless steels via laser powder bed fusion: insights from neutron diffraction and machine learning
Sofras, C., Čapek, J., Leinenbach, C., Logé, R. E., Strobl, M., & Polatidis, E. (2024). Exploring crystallographic texture manipulation in stainless steels via laser powder bed fusion: insights from neutron diffraction and machine learning. Virtual and Physical Prototyping, 19(1), e2390483 (13 pp.). https://doi.org/10.1080/17452759.2024.2390483 |
| Machine learning for quantitative structural information from infrared spectra: the case of palladium hydride
Usoltsev, O., Tereshchenko, A., Skorynina, A., Kozyr, E., Soldatov, A., Safonova, O., … Bugaev, A. (2024). Machine learning for quantitative structural information from infrared spectra: the case of palladium hydride. Small Methods, 8(7), 2301397 (5 pp.). https://doi.org/10.1002/smtd.202301397 |
| Data-driven gradient regularization for quasi-Newton optimization in iterative grating interferometry CT reconstruction
Van Gogh, S., Mukherjee, S., Rawlik, M., Pereira, A., Spindler, S., Zdora, M. C., … Stampanoni, M. (2024). Data-driven gradient regularization for quasi-Newton optimization in iterative grating interferometry CT reconstruction. IEEE Transactions on Medical Imaging, 43(3), 1033-1044. https://doi.org/10.1109/TMI.2023.3325442 |
| Machine learning based flow regime recognition in helically coiled tubes using X-ray radiography
Breitenmoser, D., Prasser, H. M., Manera, A., & Petrov, V. (2023). Machine learning based flow regime recognition in helically coiled tubes using X-ray radiography. International Journal of Multiphase Flow, 161, 104382 (10 pp.). https://doi.org/10.1016/j.ijmultiphaseflow.2023.104382 |
| Harnessing data science to improve molecular structure elucidation from tandem mass spectrometry
Harris, E., Gasser, L., Volpi, M., Perez-Cruz, F., Bjelić, S., & Obozinski, G. (2023). Harnessing data science to improve molecular structure elucidation from tandem mass spectrometry. Structural Chemistry, 34, 1935-1950. https://doi.org/10.1007/s11224-023-02192-2 |
| Data-driven machine learning for disposal of high-level nuclear waste: a review
Hu, G., & Pfingsten, W. (2023). Data-driven machine learning for disposal of high-level nuclear waste: a review. Annals of Nuclear Energy, 180, 109452 (10 pp.). https://doi.org/10.1016/j.anucene.2022.109452 |
| Advancing enzyme’s stability and catalytic efficiency through synergy of force-field calculations, evolutionary analysis, and machine learning
Kunka, A., Marques, S. M., Havlasek, M., Vasina, M., Velatova, N., Cengelova, L., … Prokop, Z. (2023). Advancing enzyme’s stability and catalytic efficiency through synergy of force-field calculations, evolutionary analysis, and machine learning. ACS Catalysis, 13(19), 12506-12518. https://doi.org/10.1021/acscatal.3c02575 |
| Machine learning for classifying narrow-beam electron diffraction data
Matinyan, S., Demir, B., Filipcik, P., Abrahams, J. P., & van Genderen, E. (2023). Machine learning for classifying narrow-beam electron diffraction data. Acta Crystallographica Section A: Foundations and Advances, 79, 360-368. https://doi.org/10.1107/S2053273323004680 |
| Deep learning denoiser assisted roughness measurements extraction from thin resists with low signal-To-noise ratio (SNR) SEM images: analysis with SMILE
Sacchi, S., Dey, B., Mochi, I., Halder, S., & Leray, P. (2023). Deep learning denoiser assisted roughness measurements extraction from thin resists with low signal-To-noise ratio (SNR) SEM images: analysis with SMILE. In P. P. Naulleau, P. A. Gargini, T. Itani, & K. G. Ronse (Eds.), Proceedings of SPIE - the international society for optical engineering: Vol. 12750. International conference on extreme ultraviolet lithography (p. 1275010 (12 pp.). https://doi.org/10.1117/12.2687639 |
| Analyses of the bias and uncertainty of SNF decay heat calculations using Polaris and ORIGEN
Shama, A., Caruso, S., & Rochman, D. (2023). Analyses of the bias and uncertainty of SNF decay heat calculations using Polaris and ORIGEN. Frontiers in Energy Research, 11, 1161076 (18 pp.). https://doi.org/10.3389/fenrg.2023.1161076 |
| Prediction of 2G HTS tape quench behavior by random forest model trained on 2-D FEM simulations
Sotnikov, D., Lyly, M., & Salmi, T. (2023). Prediction of 2G HTS tape quench behavior by random forest model trained on 2-D FEM simulations. IEEE Transactions on Applied Superconductivity, 33(5), 6602005 (5 pp.). https://doi.org/10.1109/TASC.2023.3262212 |
| Jets and jet substructure at future colliders
Bonilla, J., Chachamis, G., Dillon, B. M., Chekanov, S. V., Erbacher, R., Gouskos, L., … Yu, S. S. (2022). Jets and jet substructure at future colliders. Frontiers in Physics, 10, 897719 (17 pp.). https://doi.org/10.3389/fphy.2022.897719 |
| CPR estimation by CTF subchannel code with machine learning support
Nikitin, K., Arnold, B., Clifford, I., & Ferroukhi, H. (2022). CPR estimation by CTF subchannel code with machine learning support (p. N13P156 (10 pp.). Presented at the The 13th international topical meeting on nuclear reactor thermal hydraulics, operation and safety (NUTHOS-13). . |
| Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and <em>operando</em> X-ray radiography guidance
Pandiyan, V., Masinelli, G., Claire, N., Le-Quang, T., Hamidi-Nasab, M., de Formanoir, C., … Wasmer, K. (2022). Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance. Additive Manufacturing, 58, 103007 (15 pp.). https://doi.org/10.1016/j.addma.2022.103007 |
