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