Fast Battery Solids
The electrification of mobility, energy generation, and industry is considered an important step towards the decarbonization of the economy and combating climate change. Solid-state batteries are regarded as the next milestone in battery research. By replacing the flammable liquid electrolyte in current lithium-ion batteries with a solid and lithium-conducting component, the solid-state battery promises improved safety, excellent stability, and a long lifespan. However, it takes between 8 and 15 years to optimize a SSE (solid-state electrolyte) candidate in terms of structure and stability for a particular material class.
By intelligently and selectively combining the accuracy of proven calculation methods such as density functional theory (DFT) and molecular dynamics (MD) with the flexibility and speed of data science approaches (machine learning (ML)), the methods developed here will enable users to efficiently search the essentially infinite space of solid electrolyte material compositions to identify the unique properties and chemical compositions that best meet the desired functional requirements along the battery life cycle. To address the challenges associated with the experimental validation of computationally and data-scientifically obtained results, Raman spectroscopy is proposed as a cost-effective, non-destructive, and easily accessible characterization method. In addition, the project aims to asynchronously orchestrate the way the discovery process itself is coordinated, using a digital material acceleration platform (MAP).
