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
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)
Deciphering High-Order Structural Correlations within Fluxional Molecules from Classical and Quantum Configurational Entropy
J Chem Theory Comput
(2020)
16
6785
(doi: 10.1021/acs.jctc.0c00642)
Committee neural network potentials control generalization errors and enable active learning
The Journal of chemical physics
(2020)
153
104105
(doi: 10.1063/5.0016004)
Deciphering High-order Structural Correlations within Fluxional
Molecules from Classical and Quantum Configurational Entropy
(2020)
Converged quantum simulations of reactive solutes in superfluid helium: The Bochum perspective
The Journal of chemical physics
(2020)
152
210901
(doi: 10.1063/5.0008309)
Committee neural network potentials control generalization errors and
enable active learning
(2020)
Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground.
Journal of chemical theory and computation
(2019)
16
88
(doi: 10.1021/acs.jctc.9b00805)
Quantum nature of the hydrogen bond from ambient conditions down to ultra-low temperatures
Physical Chemistry Chemical Physics
(2019)
21
24967
(doi: 10.1039/c9cp04795f)
- ‹ previous
- Page 5
