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Research Associate


Water remains the most fascinating liquid in our world. In order to provide new insights into aqueous phase behavior, I am developing machine learning methodology trained on accurate electronic structure calculations. In close collaboration with experiment, I apply these models to water in complex environments. Overall, my research aims at the modelling of aqueous systems at previously unreachable accuracy to provide reliable structural and dynamical insights into the aqueous phase.



  • Ruhr-University Bochum, Bachelor (2013) & Master (2015) of Science in Chemistry
  • Stanford University, Visiting graduate student in the group of Prof. Thomas Markland


  • Ruhr-University Bochum, PhD (2019) in the group of Prof. Dominik Marx, Title: "Properties of Hydrogen Bonding at Ultra-low Temperatures in Bosonic Quantum Solvents"
  • Charles University Prague, PostDoc in the group of Dr. Ondrej Marsalek

Selected Articles

"Unravelling the influence of quantum proton delocalization on electronic charge transfer through the hydrogen bond" C. Schran, O. Marsalek, T. E. Markland, Chem. Phys. Lett. 2017, 678, 289-295

"Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground" C. Schran, J. Behler, D. Marx, J. Chem. Theo. Comp. 2020, 16 (1), 88-99

"Committee neural network potentials control generalization errors and enable active learning" C. Schran, K. Brezina, O. Marsalek, J. Chem. Phys. 2020, 153 (10), 104105

"Machine learning potentials for complex aqueous systems made simple" C. Schran, F. L. Thiemann, P. Rowe, E. A. Müller, O. Marsalek, A. Michaelides, Proc. Natl. Acad. Sci. 2021, 118 (38), e2110077118


Machine learning potentials for complex aqueous systems made simple
C Schran, FL Thiemann, P Rowe, EA Müller, O Marsalek, A Michaelides
– Proc Natl Acad Sci U S A

Postdoctoral researcher

Telephone number

01223 336384 (shared)

Email address