Department of Engineering / Profiles / Professor Gábor Csányi

Department of Engineering

Professor Gábor Csányi

gc121

Gábor Csányi

Professor of Molecular Modelling

Academic Division: Mechanics, Materials and Design

Research group: Applied Mechanics

Telephone: +44 1223 7 66966

Email: gc121@eng.cam.ac.uk

Publications


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. 

I help run an informal meeting called Machine Learning Discussion Group (MLDG) where we discuss the application of machine learning to physics, chemistry and materials science problems. You can subscribe to the MLDG mailing list (with a current Cambridge network ID), and get information about the talks

Audio and Video

"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.