Hannes Kneiding

Hannes Kneiding


PhD candidate

Research group | Hylleraas Centre for Quantum Molecular Sciences RT5
Main supervisor | David Balcells
Co-supervisors | R. De Bin & T.B. Pedersen
Affiliation | Department of Chemistry, UiO
Contact | hanneskn@uio.no

Short bio

My academic background is computational physics with a focus on computational statistics and quantum chemistry. In my master thesis I worked on genetic algorithms for optimizing molecular properties in a given chemical space.

Research interests and hobbies

My main research interests lie in the development and employment of predictive as well as generative machine learning models for chemical applications based on quantum chemistry data.

CompSci project

Project 1.4

Catalyst discovery by combining computational chemistry with machine learning


The starting point of this project is the tmQM data set which consists of approximately 86k data points of transition metal complexes, each featuring electronic structure data and quantum property data. Using this data set machine learning approaches shall be applied in order to build predictive as well as generative models for transition metal complexes. One of the main aspects therein is the generation of appropriate representations of molecules in terms of graphs that incorporate the relevant physics in an efficient way. These graph representations in turn call for special neural network architectures (message passing neural networks) that can handle such structurally diverse data structures. Corresponding preliminary work for organic molecules exists and one open research question is how the performance for transition metal complexes compares to these results.



CompSci publications

None yet.

Previous publications

None yet.


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Published Nov. 2, 2021 4:32 PM - Last modified Nov. 2, 2021 4:32 PM