Neural Networks Speed Up Search For Solid-State Battery Materials For Safer Electric Cars With Extended Range

Researchers from
Skoltech and AIRI Institute have shown how machine learning
can speed up the development of new materials for
solid-state lithium-ion batteries. These are an emerging
energy storage technology, which could theoretically replace
conventional Li-ion batteries in electric vehicles and
portable electronics, reducing fire hazards and extending
battery life. In the Russian Science Foundation-backed
study, published in npj Computational Materials,
neural networks proved capable of identifying promising
materials for the key component of these advanced batteries
— the solid electrolyte — as well as for its protective
coatings.

Like its conventional counterpart, the
solid-state battery incorporates an electrolyte, through
which ions carrying the electric charge travel from one
electrode to another. While in a conventional battery the
electrolyte is a liquid solution, its solid-state analogue,
as the name suggests, relies on solid electrolytes, such as
ceramics, to conduct lithium ions.

So far solid-state
batteries have not been adopted by carmakers, but EV
developers are looking to capitalize on the technology
before competitors. The new type of energy storage could
improve fire safety and boost EV range by up to 50%. The
problem is that none of the currently available solid
electrolytes meets all the technical requirements. So the
search for new materials continues.

“We demonstrated
that graph neural networks can identify new solid-state
battery materials with high ionic mobility and do it orders
of magnitude faster than traditional quantum chemistry
methods. This could speed up the development of new battery
materials, as we showed by predicting a number of protective
coatings for solid-state battery electrolytes,” commented
the lead author of the study, Artem
Dembitskiy, a PhD student of Skoltech’s Materials
Science and Engineering program, a research intern at
Skoltech Energy, and a junior research scientist at AIRI
Institute.

Study co-author, Assistant Professor
Dmitry Aksyonov from Skoltech Energy
explained the role of protective coatings: “The metallic
lithium of the anode is a strong reducing agent, so almost
all existing electrolytes undergo reduction in contact with
it. The cathode material is a strong oxidizing agent. When
oxidized or reduced, electrolytes lose their structural
integrity, which can degrade performance or even cause a
short circuit. You can avoid this by introducing two
protective coatings that are stable in contact with the
anode and the electrolyte and the cathode and the
electrolyte.”

Machine learning algorithms make it
possible to accelerate the calculation of ionic
conductivity, a key property both for electrolytes and for
protective coatings. It is among the most computationally
challenging characteristics calculated in screening the
candidate materials. For protective coatings, the list of
properties that are checked at various stages of material
selection includes thermodynamic stability, electronic
conductivity, electrochemical stability, compatibility with
electrode and electrolyte materials, ionic conductivity, and
so on. Such screening happens in stages and gradually
narrows down the list of perhaps tens of thousands of
initial options to just a few materials.

The authors
of the study used their machine learning-accelerated
approach to search for coating materials to protect one of
the most promising solid-state battery electrolytes:
Li10GeP2S12. The search identified multiple promising
coating materials, among them the compounds Li3AlF6 and
Li2ZnCl4.

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