Department of Engineering / Profiles / Professor Gábor Csányi

Department of Engineering

Professor Gábor Csányi


Gábor Csányi

Professor of Molecular Modelling

Academic Division: Mechanics, Materials and Design

Research group: Applied Mechanics

Telephone: +44 1223 7 66966


Research interests

My expertise is in atomistic simulation, particularly in multi scale modelling that couples quantum mechanics to larger length scales. I am currently engaged in applying machine learning and other data intensive techniques to materials modelling problems, such as deriving force fields (interatomic potentials) from ab initio electronic structure data. Also interested in statistical problems in molecular dynamics, e.g. in enhanced sampling algorithms that can be used explore the global configuration space of materials and molecules. 

MACE-MP-0 is a model fitted to small inorganic crystals, and is capable of performing molecular dynamics for arbitrary materials and molecules.

At the moment (2024) my research group is particularly enthralled by the potential of "foundation models" for atomistic chemistry, applicable across the periodic table.


I help run the Lennard-Jones Centre, which has a seminar series where we often discuss the application of machine learning to physics, chemistry and materials science problems. 

Audio and Video

"Machine learning potentials: from polynomials to message passing networks" talk at IPAM in April, 2023.  

"Machine learning the quantum mechanics of materials and molecules" in July 2020 at the Ellis workshop on "Quantum and physics based machine learning"

An online seminar from June 2020 on machine learned interatomic potentials, quite technical in content, in the ML4Science series, with the now recording on YouTube

A seminar at IPAM on machine learning and force fields for materials and molecules, part of the "Machine Learning for Physics and the Physics of Learning" programme in the fall of 2019. 

Listen to a podcast about this work, part of the Materials and Megabytes series!

seminar in Edinburgh at ICMS given in the spring of 2019 on machine learning, materials science, and the Gaussian Approximation Potential (GAP) models. 

A podcast by Physics World (my bit starts at around 22:25, 12 minutes long) in which the general idea machine learning in the physical sciences is discussed, along with open access publishing (October 2019). 

A much older seminar (2015) at IPAM as part of the long program on machine learning for many-particle systems.