Publikationen von Sina Stocker

Jung, H., L. Sauerland, S. Stocker, K. Reuter und J. Margraf: Machine-learning driven global optimization of surface adsorbate geometries. npj Computational Materials 9, 114 (2023).

Stocker, S., H. Jung, G. Csányi, C.F. Goldsmith, K. Reuter und J. Margraf: Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration. Journal of Chemical Theory and Computation 19 (19), 6796–6804 (2023).

Stocker, S., J. Gasteiger, F. Becker, S. Günnemann und J. Margraf: How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? Machine Learning: Science and Technology 3 (4), 045010 (2022).

Stocker, S.: Transferability in chemical machine learning. Technische Universität München