Rate My Professor Bernd Ensing

Ensing's academic interests center on developing multiscale modeling and machine learning techniques to simulate chemical processes, explore free energy landscapes, and design molecules and materials for sustainable applications. His work employs neural networks trained on accurate quantum chemical data to predict reactivity, synthesis routes, and properties for catalysis, drug discovery, CO2 capture, and biodegradable polymers. He has secured key honors including the HRSMC Fellowship in 2016, an eScience grant in 2023, and collaborative funding such as Gravitation ANION, MMD hub grant, and HyPRO in 2024. Representative publications include "Learning Neural Free-Energy Functionals with Pair-Correlation Matching" (Physical Review Letters, 2025), "Simultaneous sampling of multiple transition channels using adaptive paths of collective variables" (Journal of Chemical Physics, 2025), "Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks" (Journal of Chemical Theory and Computation, 2021), "Fast Proton Transport in FeFe Hydrogenase via a Flexible Channel and a Proton Hole Mechanism" (Journal of Physical Chemistry B, 2022), and the seminal "Metadynamics as a tool for exploring free energy landscapes of chemical reactions" (Accounts of Chemical Research, 2006). His research has garnered over 5,000 citations, influencing computational chemistry and AI-driven scientific discovery.