When searching for new materials to boost battery efficiency, the tools offered by computational chemistry have great potential to turn this task into a fast, inexpensive and efficient process. 

Lacking better strategies, the search for new materials has traditionally consisted of relying on chemical intuition to first select the systems with the best properties, then synthesize them in the laboratory and finally measure their viability in a process of trial and error that was not very efficient, essentially based on a serendipity that was not always there.

Today´s theoretical chemistry makes it possible to turn this process based on intuition and serendipity into a rational search, based on real calculations and without discarding potentially valid structures a priori.

This is possible thanks to the physical basis of current quantum-chemical theories, which allow us to describe, theoretically, the behavior of materials and their properties. In other words, it is possible to predict how a given material may respond without the need to physically dispose of it.

This optimized prospecting of new materials for energy storage represents a cost advantage, and is, at the same time, the cornerstone to improve competitiveness in a key sector for society and crucial to achieve the objectives of the energy transition.

In order to better explain what these techniques consist of, we can use as an example the argyrodite, a mineral that CIC energiGUNE works with and on which there are numerous theoretical works. It is a crystalline solid with interesting properties to be used as electrolyte in solid-state batteries. This is because it contains channels in its structure through which the migration of ions such as Li+, in the case of lithium batteries, is possible.

Argyrodites, as found in nature, are canonically made up of one or two metals, typically silver (Ag) and germanium (Ge), and a chalcogen which is usually sulfur (S). However, would combinations between other metals also be stable? And with other chalcogens, such as oxygen (O) or selenium (Se)? Could the latter be replaced by halogens (F, Cl, Br or I)?

If we do not exclude in advance any metal, halogen or chalcogen, there are millions of possible combinations, which makes it impossible to comprehensively synthesize all these structures in the laboratory and measure their properties.

This is where the potential of computational simulation studies comes in, allowing the efficient identification of new combinations with new properties to open up a range of possibilities for the industry.

Stages in the computational design of a material

When carrying out a computational simulation study of a new material, we will first study the stability of the material.

Continuing with our previous example, to check whether one of these systems would maintain the structure of argyrodite after having been synthesized, an optimization of its geometry is performed.

Thus, we start by calculating the total energy of the system with the new atoms in the known positions for the argyrodite. Then, the atomic positions are slightly varied and the energy of the new geometry is calculated: if it is lower than the previous one, this new structure will be more stable and therefore this step must be repeated. 

Although the process is more complex than described here, the calculation is finished when the new geometries do not further reduce the total energy of the system: that is, the calculation will have converged.

This process involves several steps to find the geometrical configuration with the lowest energy and, therefore, the most stable, which varies depending on the degree of precision we seek in our analysis and the convergence criteria we impose. Thanks to this computational process, in general, a structure can be optimized in a few hours or a few days.

Once we confirm that the new structure has the optimal geometry and has not deviated too much from the argyrodite configuration, the next step is to analyze the cost (in energetic terms) of the lithium atoms migrating through the structure, which can be evaluated in a matter of seconds.

With these two analyses, in approximately two or three days we can have a physically based criterion for whether a structure is worth further theoretical study and subsequent experimental study. All this through a completely computational process, with zero environmental and human impact, and with a minimum economic cost

An opportunity for industry

As already mentioned, the savings in costs, resources and time that computational chemistry represents for materials analysis is a very valuable asset for the development of the industry.

Even more so if we take into account that, thanks to its theoretical nature and its lack of need for physical resources, it allows the development of an even more sustainable activity, reducing the need to extract materials that can later be discarded.

At this moment, from CIC energiGUNE we are already executing projects with leading European companies in electrical energy storage where the procedure described in this article is applied, with the aim of evaluating the optimal composition of crystalline solid materials that will be used as electrolytes in the aforementioned solid-state batteries

In short, computational chemistry represents an opportunity to achieve more efficient materials and batteries, which not only mean an improvement in costs and efficiency for the industry, but also help to improve the sustainability of the planet and the care of its nature.

Author: Dr. Alfonso Gallo, postdoctoral researcher of the Computational Simulation research group at CIC energiGUNE.

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