Hannes Kneiding

About me

My name is Hannes Kneiding. I am a university graduate with a PhD in Computational Science obtained at the University of Oslo. I am interested in computational materials science based on quantum-mechanical calculations. My main focus so far lies on the employment of machine learning methods in conjunction with approaches from quantum chemistry for the investigation of transition metal complexes.

Work experience

since 2026	MSCA postdoctoral fellow
	University of Oxford
2024 – 2026	(Independent) researcher
	Universitetet i Oslo
2020 – 2021	Working student
	CreativeQuantum GmbH
2019 – 2020	Teaching assistant for computer science
	Freie Universität Berlin
2016 – 2018	Working student
	AnaTox GmbH & Co. KG

Education

2021 – 2024	PhD Computational Science
	Universitetet i Oslo
2017 – 2021	M.Sc. Computational Sciences
	Freie Universität Berlin
2018 – 2019	ERASMUS+ year abroad
	Universitetet i Oslo
2014 – 2017	B.Sc. Bioinformatics & Biosystemsengineering
	University of applied Sciences Wildau

Selected Publications

• tmQMg* Dataset: Excited State Properties of 74k Transition Metal Complexes

  Kneiding, H., & Balcells, D. — Journal of Chemical Information and Modeling, 2025

• A deep generative model for the inverse design of transition metal ligands and complexes

  Strandgaard, M., Linjordet, T., Kneiding, H., Burnage, A. L., Nova, A., Jensen, J. H., & Balcells, D. — JACS Au, 2025

• Directional multiobjective optimization of metal complexes at the billion-system scale

  Kneiding, H., Nova, A., & Balcells, D. — Nature Computational Science, 2024

• Deep learning metal complex properties with natural quantum graphs

  Kneiding, H., Lukin, R., Lang, L., Reine, S., Pedersen, T. B., De Bin, R., & Balcells, D. — Digital Discovery, 2023

Full list on Google Scholar →

Software & Data

Software

• HyDGL

  Python package for generating quantum-chemistry informed graphs of transition metal complexes.

• uxtbpy

  Unofficial API for interfacing xTB with Python.

• PL-MOGA

  Multiobjective genetic algorithm for the directed exploration of transition metal complex chemical spaces.

Datasets

• tmQMg

  Graph dataset of mononuclear transition metal complexes found in the Cambridge Structural Database.

• tmQMg-L

  Ligand dataset extracted from tmQMg with estimated formal charges as well as steric, electronic and cheminformatics descriptors.

• tmQMg*

  Excited state dataset of most transition metal complexes found in tmQMg including charge transition character as well as solvatochromism data.

Theses

2024	PhD Thesis
	Complex Learning - Machine learning approaches for transition metal complexes
2020	Master Thesis
	Developing high performance materials through quantum mechanical simulations and genetic algorithms
2017	Bachelor Thesis
	Software Validation for the Calibration of Bioanalytical Systems (German)