Christoph studied Chemistry in Bochum, where he obtained his PhD in 2019, during which he participated in various exchanges and research visits to Glasgow, Stanford University, and the École Normale Supérieure (ENS) in Paris. After completing his PhD, he briefly moved to Prague before taking up a postdoctoral research fellowship in London in 2020. In October 2020, he transitioned to Cambridge, where he began working with Angelos Michaelides on using machine learning to understand complex aqueous systems.
In 2023, Christoph started his independent career as an Assistant Professor in Computational Atomic-Scale Materials Science at the Cavendish Laboratory, leading the Frontiers in Atomistic Simulation Techniques group. Together with a team of computational scientists, he studies challenging materials, primarily involving water, using advanced computational methods. Recently, his major focus has been on developing machine learning potentials to provide insights into complex aqueous systems, for which accurate and efficient representations of potential energy surfaces are urgently needed.
In addition to his academic work, Christoph serves as deputy director of the MPhil in Scientific Computing, supporting students with an interest in atomistic simulation techniques. He is also an active member of the management team of the Lennard-Jones Centre, which unites the molecular and materials modelling community in Cambridge. In his spare time, he enjoys brewing coffee and beer, as well as hiking and participating in parkruns.
Selected Publications:
N. O’Neill, B.X. Shi, K. Fong, A. Michaelides, C. Schran, To pair or not to pair? Machine-learned explicitly-correlated electronic structure for NaCl in water, The Journal of Physical Chemistry Letters, 15, 6081-6091 2024
V. Kapil, C. Schran, A. Zen, J. Chen, C.J. Pickard, A. Michaelides, The first-principles phase diagram of monolayer nanoconfined water, Nature, 609, 512–516 2022
C. Schran, F.L. Thiemann, P. Rowe, E.A. Müller, O. Marsalek, A. Michaelides, Machine learning potentials for complex aqueous systems made simple, Proceedings of the National Academy of Sciences, 118 (38), e2110077118 2021
C. Schran, K. Brezina, O. Marsalek, Committee neural network potentials control generalization errors and enable active learning, The Journal of Chemical Physics, 153 (10), 104105 2020