DHL Relay 2017

EU FET Open project LiRichFCC: New Battery concept promises more compact energy storage

Contact Head of Section

Tejs Vegge
Professor, Head of Section
DTU Energy
+45 45 25 82 01

Contact Section Secretary

Karina Ulvskov Frederiksen
Section secretary
DTU Energy
+45 45 25 82 02

Research focus

The scientific focus in Section for Atomic Scale Modelling and Materials (ASC) is centered on computational design and characterization of materials for energy conversion and storage, based on a detailed atomic-scale understanding of their structure and kinetics. An essential aspect of our work is the development and application of novel computational approaches, which are linked closely to experimental in situ structural and electrochemical characterization.

The two main research areas in ASC are Next-generation battery materials and Electrocatalystic reactions and materials, but the section has several other activities, including Solid-state storage of gas-phase energy carriers, Solar cells and photocatalysis, and Resistive switching memories. Common for the different research areas is a shared computational framework based on Computational screening and prediction of composition/structure and Ionic and electronic transport mechanisms.

Publikation 2
Orientation-Dependent Oxygen Evolution on RuO2 without Lattice Exchange


Kelsey A. Stoerzinger, et al. ACS Energy Lett., 2017, 2, 876-881.


Read the paper here
In collaboration with Prof. Yang Shao-Horn’s group from MIT, we have examined the OER kinetics on rutile RuO2 in the (110), (100), (101), and (111) orientations, finding (100) the most active. We find no evidence of oxygen exchange in acidic or basic electrolytes, suggesting it is not exchanged in catalyzing OER on crystalline RuO2 surfaces. This is supported by the correlation of activity with the number of active Ru-sites calculated by DFT, where more active facets bind oxygen more weakly. This new understanding provides a design strategy to enhance the OER activity of RuO2.
Publikation 1
Computational Study of Nb-Doped-SnO2/Pt Interfaces: Dopant Segregation, Electronic Transport, and Catalytic Properties

Qiang Fu, et al. Chem. Mater, 2017, 29, 1641-1649.


Read the paper here

We have analyzed Nb-doped tin dioxide (NTO) as an alternative support for Pt-based catalysts in PEMFCs. Through a combined DFT and non-equilibrium Green’s function study, we investigate the Nb segregation at Pt/NTO interfaces under operational conditions, and reveal the effects on the electronic transport and catalytic properties. We find that the Nb dopants tend to aggregate in the subsurface layers of the NTO substrate, whereas their transport across the Pt/NTO interface is hindered. This understanding will help shed light on future applications of Nb/Sb-doping in SnO2.
Addressing uncertainty in atomistic machine learning


A. Peterson, R. Christensen, and A. Khorshidi. Phys. Chem. Chem. Phys,. 2017, 19, 10978-10985


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Machine-learning regression can emulate more expensive electronic-structure calculations of potential energies and forces. Here, neural network calculators are trained to emulate another calculator based on a set of training images. However, the uncertainty of a given machine-learning caluculator is unknown outside the not always well-defined area of training. We here demonstrate how an ensemble of trained neural network calculators can be used to estimate the uncertainty in calculations with trained neural networks. 
Role of CO* as a Spectator in CO2 Electroreduction on RuO2

A. Bhowmik, H. A. Hansen and

T. Vegge. J. Phys. Chem. C, 2017, 121 (34), pp 18333-18343

Read the paper here.

Employing density functional theory based computational electrocatalysis models we studied binding energy amendment due to to adsorbate interaction (steric and electronic) with varying coverage of CO* spectators on the catalyst surface. Implications on the reaction pathways help us rationalize differences in experimentally observed carbonaceous product mix and suppression of the hydrogen evolution reaction (HER). We show that a moderate CO* coverage (∼50%) is necessary for obtaining methanol as a product and that higher CO* coverages leads to very low overpotential for formic acid evolution.