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ASM family fun day in Fælledparken; June 2020

BIG-MAP: Battery Interface Genome - Materials Acceleration Platform

The ASM section trying out curling.

DELIGHT: DEep LearnIng Green cHemical caTalysis

New position - Associate Professor Piotr de Silva

New position - Associate Professor Ivano E. Castelli

Contact Head of Section

Tejs Vegge

Professor, Head of Section

DTU Energy

+45 45 25 82 01
teve@dtu.dk

Contact Section Secretary

Karina Ulvskov Frederiksen

Administrative Coordinator

DTU Energy

+45 93 51 17 47
karf@dtu.dk

The Section for Atomic Scale Materials Modelling performs atomic-scale simulations of materials and reactions in the field of energy conversion and storage. We target the development and application of novel methodologies to facilitate accelerated computational design and discovery of clean energy materials directly for their intended operating conditions. The section unites competences in computational materials physics and chemistry, method development in electronic structure theory, computational screening and big data analytics.

The main research interests of the section are:

computational atomic-scale characterization, design and discovery of materials for electrochemical conversion and storage of clean energy, especially within next-generation batteries and materials for sustainable production of electrofuels and chemicals
modelling of nanostructured materials, in-/organic energy materials and dynamic interfacial processes
algorithms to improve the efficiency of simulations of complex processes in energy materials, such as ionic and electronic transport and light absorption, e.g., through uncertainty quantification
accelerated materials discovery through the development of a common data infrastructure and autonomous workflows for autonomous materials innovation
using machine/deep learning to analyze and orchestrate large-scale generation, storage and analysis of data from multi-scale and multi-modal computer simulations, structural and electrochemical characterization, synthesis, manufacturing, and testing.

Toward Better and Smarter Batteries by Combining AI with Multisensory and Self-Healing Approaches

Tejs Vegge, Jean-Marie Tarascon, Kristina Edström

Adv. Energy Mater., 2021, 2100362

Read more here: https://doi.org/10.1002/aenm.202100362

Creating a holistic, closed-loop infrastructure for materials discovery, manufacturing, and battery testing that utilizes a common data infrastructure and autonomous workflows, can transform battery discovery. By embedding multisensory and self-healing capabilities and integrating these with AI and physics-aware machine learning models capable of predicting the spatio-temporal evolution of battery materials and interfaces, it will be possible to identify, predict and prevent potential degradation and failure modes. This will facilitate enhanced battery quality, reliability, and life, and enable inverse design of new battery materials, interfaces, and additives.

1D metal-dithiolene wires as a new class of bi-functional oxygen reduction and evolution single-atom electrocatalysts

Qingming Deng, Jin Han, Jiong Zhao, Guibin Chen, Tejs Vegge, Heine Anton Hansen

Journal of Catalysis, 2021, Pages 140-148

Read more here (open access): https://doi.org/10.1016/j.jcat.2020.11.016

http://pubs.acs.org/doi/abs...

We use density functional theory to investigate the oxygen reduction reaction (ORR) and oxygen evolution reactions (OER) on one-dimensional metal-dithiolene wires. We find that Co, Rh, and to a lesser extent, Fe exhibits intrinsically high bi-functional ORR/OER catalytic activity in metal-dithiolene wires. Low limiting overpotentials for these catalysts arise from favorably modified scaling relations. By applying uniaxial strain, shifts in metal d-band centers allow the adsorption strengths of intermediates to be optimized for improved activity.

http://pubs.acs.org/doi/abs...

Structural and chemical mechanisms governing stability of inorganic Janus nanotubes

Felix T. Bölle, August E. G. Mikkelsen, Kristian S. Thygesen, T. Vegge, and Ivano E. Castelli
npj Computational Materials, volume 7, Article number: 41, 2021

One-dimensional inorganic nanotubes hold promise for technological applications due to their distinct physical/chemical properties. In this work we investigate, using Density Functional Theory (DFT) calculations, the formation mechanism of 135 different inorganic nanotubes formed by the intrinsic self-rolling driving force found in asymmetric 2D Janus sheets. From our pool of candidate structures we have identified more than 100 tubes with a preferred radius below 35 Å, which we hypothesize can display distinctive properties compared to their parent 2D monolayers.

Molecular modeling of organic redox-active battery materials

Rocco P. Fornari, Piotr de Silva

Wiley, WIREs Comput Mol Sci. 2021; 11:e1495.

Read more here: https://doi.org/10.1002/wcms.1495

The search for organic electroactive materials that could be used for energy storage in mobile and stationary applications is an active area of research. Computer simulations are used extensively to improve the understanding of the fundamental processes in the existing materials and to accelerate the discovery of new materials with improved performance. After introducing the fundamentals of computational organic electrochemistry, we will survey its most recent applications in organic battery research and outline some of the remaining challenges for the development and applications of atomic-scale modeling techniques in the organic battery context.

Ab initio Molecular Dynamics Investigations of the Speciation and Reactivity of Deep Eutectic Electrolytes in Aluminum Batteries

David Carrasco-Busturia, Steen Lysgaard, Piotr Jankowski, Tejs Vegge, Arghya Bhowmik, Juan María García-Lastra

ChemSusChem 2021, 14, 2034–2041
Read more here: https://doi.org/10.1002/cssc.202100163

We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments/calculations to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration–exploitation strategies. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.

Computational Design of Ductile Magnesium Alloy Anodes for Magnesium Batteries

Smobin Vincent, Jin Hyun Chang, and Juan María García Lastra.
Batteries & Supercaps (2020).

You can read the paper here

The brittleness of Mg makes it extremely difficult to produce thin foils for practical battery applications. Alloying with small amount of dopants can improve the ductility of Mg. We performed a computational screening of dopants that can alloy with magnesium and use as an anode in magnesium batteries. Three properties were evaluated for this screening: ductility improvement, stability of the alloy and propensity of dopant to reside in bulk to avoid electrochemical reactions. We considered 34 dopants and identified 12 dopants that are suitable for magnesium battery applications.