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Research Associate

Research

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.


Education

Undergratuate

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

Postgraduate

  • 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

Publications

Tracking single adatoms in liquid in a Transmission Electron Microscope.
N Clark, DJ Kelly, M Zhou, Y-C Zou, CW Myung, DG Hopkinson, C Schran, A Michaelides, R Gorbachev, SJ Haigh
– Nature
(2022)
1
Water Flow in Single-Wall Nanotubes: Oxygen Makes It Slip, Hydrogen Makes It Stick
FL Thiemann, C Schran, P Rowe, EA Müller, A Michaelides
– ACS Nano
(2022)
16,
10775
Machine learning potentials for complex aqueous systems made simple
C Schran, FL Thiemann, P Rowe, EA Müller, O Marsalek, A Michaelides
– Proceedings of the National Academy of Sciences of the United States of America
(2021)
118,
e2110077118
Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer.
C Schran, F Brieuc, D Marx
– The Journal of Chemical Physics
(2021)
154,
051101
Deciphering High-Order Structural Correlations within Fluxional Molecules from Classical and Quantum Configurational Entropy
R Topolnicki, F Brieuc, C Schran, D Marx
– J Chem Theory Comput
(2020)
16,
6785
Committee neural network potentials control generalization errors and enable active learning.
C Schran, K Brezina, O Marsalek
– The Journal of Chemical Physics
(2020)
153,
104105
Converged quantum simulations of reactive solutes in superfluid helium: The Bochum perspective
F Brieuc, C Schran, F Uhl, H Forbert, D Marx
– Journal of Chemical Physics
(2020)
152,
210901
Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground
C Schran, J Behler, D Marx
– J Chem Theory Comput
(2019)
16,
88
Quantum nature of the hydrogen bond from ambient conditions down to ultra-low temperatures
C Schran, D Marx
– Physical chemistry chemical physics : PCCP
(2019)
21,
24967
Converged Colored Noise Path Integral Molecular Dynamics Study of the Zundel Cation Down to Ultralow Temperatures at Coupled Cluster Accuracy.
C Schran, F Brieuc, D Marx
– Journal of chemical theory and computation
(2018)
14,
5068
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Postdoctoral researcher

Telephone number

01223 763872 (shared)

Email address