Department of Applied Physics

Computational Electronic Structure Theory (CEST)

The Computational Electronic Structure Theory Group is developing electronic structure and machine learning methods and applies them to pertinent problems in material science, surface science, physics, chemistry and the nano sciences.
A screenshot showing the CEST group in a Zoom meeting.
Left to right: L. Keller, P. Rinke, B. G.-Väisänen (Coord.), A. Seidu, R. Ibragimova, M. Dvorak, M. Kuchelmeister, J. Laakso, K. Ghosh, V. Havu, T. Rossi, D. Golze, X. Chen, L. Fang, V. Härkönen, J. Löfgren, J. Järvi, H.-P. Komsa, M. Todorovic, E. Lumiaro


Latest Publications

Fermiology of two-dimensional titanium carbide and nitride MXenes

Mohammad Bagheri, Rina Ibragimova, Hannu Pekka Komsa 2021 Physical Review B

Predicting gas-particle partitioning coefficients of atmospheric molecules with machine learning

Emma Lumiaro, Milica Todorović, Theo Kurten, Hanna Vehkamäki, Patrick Rinke 2021 Atmospheric Chemistry and Physics

Improved One-Shot Total Energies from the Linearized GW Density Matrix

Fabien Bruneval, Mauricio Rodriguez-Mayorga, Patrick Rinke, Marc Dvorak 2021 Journal of Chemical Theory and Computation

Low-Scaling GW with Benchmark Accuracy and Application to Phosphorene Nanosheets

Jan Wilhelm, Patrick Seewald, Dorothea Golze 2021 Journal of Chemical Theory and Computation

Assessing the G0W00(1) approach

Yanyong Wang, Patrick Rinke, Xinguo Ren 2021 Journal of Chemical Theory and Computation

Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization

Annika Stuke, Patrick Rinke, Milica Todorovic 2021 Machine Learning: Science and Technology

Machine learning force fields based on local parametrization of dispersion interactions

Heikki Muhli, Xi Chen, Albert P. Bartók, Patricia Hernández-León, Gábor Csányi, Tapio Ala-Nissila, Miguel A. Caro 2021 Physical Review B

Real-time time-dependent density functional theory implementation of electronic circular dichroism applied to nanoscale metal-organic clusters

Esko Makkonen, Tuomas P. Rossi, Ask Hjorth Larsen, Olga Lopez-Acevedo, Patrick Rinke, Mikael Kuisma, Xi Chen 2021 Journal of Chemical Physics
More information on our research in the Research database.
Research database

Research Group Members

Group Leader

Patrick Rinke

Patrick Rinke

Professori (Associate professor)


Ville Havu

Ville Havu

Vanhempi yliopistonlehtori

Research Fellows

Postdoctoral Researchers

Marc Dvorak

Marc Dvorak

Research Fellow
T304 Dept. Applied Physics

Joakim Löfgren

Postdoctoral Researcher

Tuomas Rossi

Academy Postdoctoral Researcher

Ville Härkönen

Postdoctoral Researcher

Doctoral and Master Students and Research Assistants

Kunal Ghosh

Kunal Ghosh

Doctoral Candidate
Probabilistic Machine Learning
Jari Järvi

Jari Järvi

Doctoral Candidate
T304 Dept. Applied Physics

Levi Keller

Doctoral Candidate
T304 Dept. Applied Physics

Emma Lehto

research assistant


Cooperation, Research & Art Published:

School of Science researchers now involved in five Centres of Excellence

SCI researchers are partners in three new Centres of Excellence: Life-Inspired Hybrid Materials, Randomness and structures, and Virtual laboratory for molecular level atmospheric transformations.
Department of Computer Science photo Matti Ahlgren
Research & Art Published:

New Academy projects will investigate drug development, superconductivity and emotional game experience

There are altogether 12 new Academy of Finland projects at the Aalto School of Science. Funding was received from all the three Academy of Finland Research Councils: Biosciences, Health and the Environment, Culture and Society and Natural Sciences and Engineering.
Picture of the poster
Research & Art Published:

CEST research acknowledged at the E-MRS Spring Meeting 2021

Jari Järvi wins the Best Poster Award at the annual Spring Meeting of the European Materials Research Society
Image of atomic force microscopy
Research & Art Published:

Novel cross-disciplinary approach for identifying complex molecular adsorbates

CEST researchers integrate Bayesian inference with scanning probe experiments to robustly detect surface adsorbate configurations.

Current Events

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