In silico design of efficient materials for next generation batteries (Mat4Bat)

International Collaboration

Status: ongoing project
Starting date 
1 April 2015
Ending date 
1 March 2020

Lithium-ion batteries have become indispensable in our daily lives, being used in nearly all electronic devices. They are also the power source in emergent electric vehicles, which represent a less polluting alternative to fossil fuel propelled cars. The electronics and computer industries invest a significant part of their budgets in developing batteries with longer lifetimes, more capacity and, at the same time, smaller in size than present ones. In the same vein, the automotive industry is searching for new batteries with energy densities similar to that of gasoline. This would allow electric vehicles to meet the growing requirements for long range and heavy duty transportation. In this respect, metal-air batteries (MABs) are promising candidates, although they suffer from several drawbacks that must be solved before they can enter the market. One of the biggest drawbacks that MABs face is the passivation (formation of a non-conducting interface) of the cathode during their discharge, which leads to the “sudden death” of the battery. Although a significant amount of experiments have been carried out to address the “sudden death” process, conclusive models that explain unambiguously the phenomenon remain elusive. These models are indispensable for designing a strategy able to circumvent the “sudden death” problem and to find new materials that help this purpose.
Ideal materials for a battery cathode should exhibit high energy density, long-term stability and high electrical conductivity. Designing materials with such properties efficiently cannot be done using Edisonian trial-and-error experimental strategies because they are too slow and expensive. It is necessary to gain a better comprehension of the basic processes taking place in the battery cathodes. Once the fundamental processes are understood, it is possible to search for the best materials candidates. An efficient way to do this is through computational screening of materials and dopants, an approach that has been shown to be very successful in other research areas such as heterogeneous catalysis and drug discovery. This is exactly what we propose to do in this project: To develop new theoretical tools and models for elucidating the fundamental nature of the electronic conduction in MABs cathodes and to find the optimal materials for achieving next generation, high capacity batteries. This approach provides an inexpensive and fast way to transform MABs from a promising technology to a reality. The latter will give electric vehicles the opportunity to compete on equal ground with fossil fuel based cars.
The research will be carried out at the Department of Energy at the Technical University of Denmark in collaboration with leading groups in the fields of battery development (Binghamton University and Massachusetts Institute of Technology in the USA) and computational simulations (University of Basque Country in Spain).


Overall, the proposed project is a good example of synergy between formal theoretical development
and applicability to practical situations where theoretical approaches, advanced numerical simulations and comparison with experimental data coexist. Below we list the specific objectives for the project, starting with the most technical one:
1. To develop and implement state‐of‐the art algorithms for describing electron conductance accurately and in a computationally affordable way. Most of these algorithms will be applicable to the study of MABs and LIBs at the same time.
The second objective is related with the comprehension of electron conduction processes in battery electrodes:
2. To unambiguously identify the mechanisms responsible for the electronic conduction in MAB cathodes.
The final objectives are related with the design of new cathode materials through computational screening:
3. To propose possible new dopants for improving the performance in LIB cathodes through a computational screening of materials. In order to construct models for MABs that are reliable they need to be tested against well characterized systems, such as the well‐known LIBs. By using LIBs as a test system, the models will allow us to study the sudden death issue in MABs, while at the same time searching for materials to improve LIB performance. Including LIBs in this project ensures a valuable output for future electric vehicle development since MABs are a high risk/high gain technology.
4. To suggest possible solutions for overcoming the so‐called “sudden death” process in each of the MABs currently under development. Again a computational screening of materials will be used for this aim.


Section for Atomic Scale Modelling and Materials at DTU Energy.
Nano-bio Spectroscopy Group. Prof. Dr, Angel Rubio Secades.
Group of Prof. Yang Shao-Horn. MIT
Group of Prof. M. Stanley Whittingham in Binghamton SUNY.

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