Gábor Csányi

Professor Dr. Gábor Csányi joined the Max Planck Society in January 2026 as Director at the Max Planck Institute for Polymer Research. His appointment is part of the Max Planck Liquids Initiative, co-funded by the state of Rhineland-Palatinate, which brings together researchers to investigate the molecular properties of liquids and interfaces, in close connection with the MaxWater Initiative of the Max Planck Society.

Originally from Hungary, Csányi studied mathematics at the University of Cambridge, where he received his Batchelor of Arts degree in Mathematics in 1994, and earned his PhD in computational physics from the Massachusetts Institute of Technology (MIT) in 2001. Following his doctoral studies, he held a Research Associate position at the Cavendish Laboratory, University of Cambridge (2001–2007). He subsequently progressed through academic positions in the Engineering Laboratory at the University of Cambridge, serving as University Lecturer, University Senior Lecturer, Reader in Engineering, and since 2016 as Professor of Molecular Modelling.

Csányi has held several visiting and honorary appointments, including Argyris Lecturer and Visiting Professor at the University of Stuttgart (2022) and August Wilhelm Scheer Visiting Professor at the Technical University of Munich (2019). His scientific achievements have been recognized with a number of awards, notably an Leverhulme Early Career Fellowship, and the F. W. Bessel Award of the Alexander von Humboldt Foundation in 2010. He was elected Fellow of the Royal Society of London in 2025.

Csányi’s research focuses on the development of computational models that describe atomic-scale interactions with high accuracy and efficiency, in particular force fields that are derived from quantum mechanical calculations. These methods aim to bridge quantum-mechanical descriptions and mesoscopic behavior, advancing the understanding of materials relevant to chemistry, biology, and materials science. His work combines physics-based modeling with machine learning approaches to enable predictive simulations, including for liquids and complex molecular systems. His early work helped define the field of machine learned interatomic potentials (MLIPs), and his group discovered the extraordinary generalization capability of `foundation models’ that are trained on a diverse set of inorganic crystals and are capable of simulating almost any kind of material and chemical process.