Rate My Professor Bernd Ensing

Bernd Ensing serves as Professor of Artificial Intelligence for Chemistry in the Faculty of Science at the University of Amsterdam, within the Van 't Hoff Institute for Molecular Sciences. Educated as a chemical engineer at the University of Twente, he obtained his PhD in theoretical chemistry from Vrije Universiteit Amsterdam in 2003 with the thesis "Chemistry in water: first principles computer simulations." This research utilized ab initio methods, Car-Parrinello molecular dynamics, and rare event sampling techniques to investigate solvation effects on organic and inorganic reaction mechanisms. He then pursued postdoctoral research at the University of Pennsylvania with Prof. Michael Klein from 2002 to 2005, exploring conformational transitions in biological systems and complex fluids via classical and coarse-grained dynamics, including metadynamics. A second postdoctoral fellowship followed at ETH Zurich with Prof. Michele Parrinello in 2005, advancing multi-scale methods for rare and non-equilibrium processes. In summer 2007, Ensing launched his independent research group at the University of Amsterdam upon receiving an NWO VIDI grant to study photoactive proteins using multiscale modeling. He advanced to full professor on 15 August 2025 and has directed the AI4Science Laboratory since 2019.

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.