Publikationen von Matteo Krüger

Krüger, M., Galeazzo , T., Eremets, I., Schmidt, B., Pöschl, U., Shiraiwa, M. und Berkemeier, T.: Improved vapor pressure predictions using group contribution-assisted graph convolutional neural networks (GC²NN), Geoscientific Model Development, 18(20), 7357–7371, doi:10.5194/gmd-18-7357-2025, 2025.

Krüger, M., Mishra, A., Spichtinger , P., Pöschl, U. und Berkemeier, T.: A numerical compass for experiment design in chemical kinetics and molecular property estimation, Journal of Cheminformatics, 16, doi: 10.1186/s13321-024-00825-0, 2024.

Berkemeier, T., Krüger, M., Feinberg, A., Müller, M., Pöschl, U. und Krieger, U. K.: Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks, Geoscientific Model Development, 16(7), 2037–2054, doi:10.5194/gmd-16-2037-2023, 2023.

Krüger, M., Wilson, J., Wietzoreck, M., Bandowe, B. A. M., Lammel, G., Schmidt, B., Pöschl, U. und Berkemeier, T.: Convolutional neural network prediction of molecular properties for aerosol chemistry and health effects, Natural Sciences, 2, doi:10.1002/ntls.20220016, 2022.

Krüger, M., Klingmüller, K., Rosanka, S., Lelieveld, J., Pöschl, U., Pozzer, A. und Berkemeier, T.: Global Health Map: Coupling EMAC and KM-SUB-ELF to estimate air pollution health effects using accurate iron soluble fractions, in EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU., 2026.

Raj, S. S., Hrabe de Angelis, I., Basic, S., Aardema, H. M., Slagter, H. A., Weber, J., Calleja, M. L., Krüger, M., Andreae, M. O., Dragoneas, A., Nillius, B., Walter, D., Berkemeier, T., Haug, G. H., Pöschl, U., Schiebel, R. und Pöhlker, C.: Exploring aerosol size distributions from polar to tropical zones of the Atlantic Ocean, in EGU General Assembly 2024, Vienna, Austria & Online., 2024.

Krüger, M.: Machine learning for the elucidation of multiphase processes and systems, Doktorarbeit, Universität, Mainz, November. [online] Available from: http://hdl.handle.net/21.11116/0000-0012-1261-A, 2025.

Krüger, M., Galeazzo , T., Eremets, I., Schmidt, B., Pöschl, U., Shiraiwa, M. und Berkemeier, T.: Improved vapor pressure predictions using group contribution-assisted graph convolutional neural networks (GC²NN), EGUsphere, doi:10.5194/egusphere-2025-1191, 2025.

Krüger, M., Mishra, A., Spichtinger , P., Pöschl, U. und Berkemeier, T.: A kinetic compass for the design of experiments to determine kinetic parameters, Research Square, doi:10.21203/rs.3.rs-3317747/v1, 2023.

Berkemeier, T., Krüger, M., Feinberg, A., Müller, M., Pöschl, U. und Krieger, U. K.: Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks, EGUsphere, doi:10.5194/egusphere-2022-1093, 2022.