Publications of Matteo Krüger

Krüger, M., Mishra, A., Spichtinger , P., Pöschl, U. and 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. and 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. and Berkemeier, T.: Convolutional neural network prediction of molecular properties for aerosol chemistry and health effects, Natural Sciences, 2, doi:10.1002/ntls.20220016, 2022.

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. and 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., Galeazzo , T., Eremets, I., Schmidt, B., Pöschl, U., Shiraiwa, M. and 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. and 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. and 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.