Current Position
Assistant Professor in Computational Atomic-Scale Materials Science at the Cavendish Laboratory, Department of Physics, University of Cambridge
Find out more about our work at https://www.fast-group.phy.cam.ac.uk/.
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
The first-principles phase diagram of monolayer nanoconfined water
(2021)
(doi: 10.48550/arxiv.2110.14569)
Machine learning potentials for complex aqueous systems made simple
– Proceedings of the National Academy of Sciences
(2021)
118,
e2110077118
(doi: 10.1073/pnas.2110077118)
Correlated Particle Motion and THz Spectral Response of Supercritical
Water
(2021)
Force-induced Catastrophes on Energy Landscapes: Mechanochemical
Manipulation of Downhill and Uphill Bifurcations Explains Ring-opening
Selectivity of Cyclopropanes
(2021)
High-dimensional neural network potentials for solvation: The case of
protonated water clusters in helium
(2021)
Converged Colored Noise Path Integral Molecular Dynamics Study of the
Zundel Cation down to Ultra-low Temperatures at Coupled Cluster Accuracy
(2021)
Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer
– Journal of Chemical Physics
(2021)
154,
051101
(doi: 10.1063/5.0035438)
Manifestations of Local Supersolidity of $^{4}$He around a Charged
Molecular Impurity
(2020)
Transferability of machine learning potentials: Protonated water neural
network potential applied to the protonated water hexamer
(2020)
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