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
Undergraduate
- Ruhr-University Bochum, Bachelor (2013) & Master (2015) of Science in Chemistry
- Stanford University, Visiting graduate student (2015) in the group of Prof. Thomas Markland
Postgraduate
- Ruhr-University Bochum, PhD (2016-2019) in the group of Prof. Dominik Marx, Title: "Properties of Hydrogen Bonding at Ultra-low Temperatures in Bosonic Quantum Solvents"
- École Normale Supérieure, Visiting graduate student (2016) in the group of Prof. Rodolphe Vuilleumier
- Charles University Prague, PostDoc (2019) in the group of Dr. Ondrej Marsalek
- University College London (2020), PostDoc in the group of Prof. Angelos Michaelides
Publications
Onset of Rotational Decoupling for a Molecular Ion Solvated in Helium: From Tags to Rings and Shells
– Physical review letters
(2023)
130,
083001
Understanding the anomalously low dielectric constant of confined water:
an ab initio study
(2022)
The first-principles phase diagram of monolayer nanoconfined water
– Nature
(2022)
609,
512
(doi: 10.1038/s41586-022-05036-x)
State-resolved infrared spectrum of the protonated water dimer: revisiting the characteristic proton transfer doublet peak
– Chem Sci
(2022)
13,
11119
(doi: 10.1039/d2sc03189b)
Infrared Spectra at Coupled Cluster Accuracy from Neural Network Representations
– Journal of chemical theory and computation
(2022)
18,
5492
(doi: 10.1021/acs.jctc.2c00511)
Neural network interaction potentials for para-hydrogen with flexible molecules.
– J Chem Phys
(2022)
157,
074302
(doi: 10.1063/5.0100953)
Tracking single adatoms in liquid in a transmission electron microscope.
– Nature
(2022)
609,
942
(doi: 10.1038/s41586-022-05130-0)
Water Flow in Single-Wall Nanotubes: Oxygen Makes It Slip, Hydrogen Makes It Stick.
– ACS nano
(2022)
16,
10775
(doi: 10.1021/acsnano.2c02784)
Neural Network Interaction Potentials for para-Hydrogen with Flexible
Molecules
(2022)
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