DTU Relay 2019 - ASM was represented with two teams

Mg-based battery technologies are one of the most promising alternatives to replace Li-ion batteries in the near future.

The ASC section trying out curling.

Battery 2030+: New large-scale and long-term research initiative in EU 

Prof. Tejs Vegge gives the keynote lecture at the Science Award Electrochemistry and Science Dialogue 2019 hosted by BASF and Volkswagen. In picture: Prof. Tejs Vegge, Prof. Doron Aurbach, Prof. Linda Nazar, Prof. Jürgen Janek, Nobel Laureate Prof. Stanley Whittingham, Prof. Martin Winter.

AiMade – A new initiative on Autonomous Materials Discovery at DTU Energy

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 (Secretariat)
DTU Energy
+45 45 25 82 02

Research focus

The scientific focus in Section for Atomic Scale Materials Modelling (ASM) 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 ASM 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.

Improving the Activity of M−N4 Catalysts for the Oxygen Reduction Reaction by Electrolyte Adsorption

Katrine L. Svane, Mateusz Reda, Tejs Vegge, Heine A. Hansen

ChemSusChem 2019, 12, 5133 – 5141

DOI: 10.1002/cssc 201902443

You can read the paper here.

In this paper, the adsorption of anions and impurities from the electrolyte on non-precious M-N4 (M = Cr, Mn, Fe, Co) catalysts for the oxygen reduction reaction (ORR) is investigated. Since the active site is located in a planar carbon sheet, adsorption can happen on one side or both sides of the metal atom. Adsorption on both sides result in poisoning, however the presence of an adsorbate on one side of the catalyst modifies the ORR activity on the remaining site. Our calculations identify combinations of metal and adsorbate that result in an improved ORR activity compared with the bare catalyst.

Improved Electrocatalytic Water Splitting Reaction on CeO2(111) by Strain Engineering: A DFT+U Study


Tiantian Wu, Tejs Vegge, Heine Anton Hansen, ACS Catalysis 2019, 9, 4853−4861 DOI: 10.1021/acscatal.9b00203

Read the paper here

By using DFT+U, we find tensile strain stabilizes the reduced states of ceria such as oxygen vacancies and surface hydroxyls, while compressive strain destabilizes the reduced states. The hydroxyl decomposition into H2 has the highest activation energy along the reaction pathway (Ea) and that the free energy of hydroxyl formation (ΔGH) prior to hydroxyl decomposition can act as a thermodynamic barrier to the water splitting reaction (WSR). Compressive strain (< -3.0%) correlates strongly with increased WSR activity on CeO2(111) because it reduces the total barrier (ΔGH+Ea).

Genetic algorithms for computational materials discovery accelerated by machine learning

Paul C. Jennings, Steen Lysgaard, Jens S. Hummelshøj, Tejs Vegge and Thomas Bligaard, (2019), Npj Computational Materials5(1), 46


You can read the paper here.

To combine the robust qualities of a genetic algorithm with rapid machine learning, we train a machine learning model on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. The approach is used to search for stable nanoalloy catalysts and, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations.

R-NEB: Accelerated Nudged Elastic Band Calculations by Use of Reflection Symmetry


Nicolai Rask Mathiesen, Hannes Jónsson, Tejs Vegge, and Juan Maria García Lastra

J. Chem. Theory Comput., 2019, 15 (5), pp 3215–3222

DOI: 10.1021/acs.jctc.8b01229


You can read the paper here.

The nudged elastic band (NEB) method is used for determining minimum energy paths of kinetic processes. Many transitions of interest occur in systems with a structural symmetry which can be exploited. Here, we present reflection-NEB (R-NEB) which identify reflection symmetric paths and make sure only the symmetry inequivalent part of path needs to be treated with explicit force calculations. This results in a speed up factor of 2. Additional speed up can be achieved if only the height of the barrier is needed in which case single image NEB calculations can often be trusted.