Exploring Li-Ion Transport Properties of Li3TiCl6: A Machine Learning Molecular Dynamics Study

What is it about?

This work shows that machine-learning models can effectively predict the behavior of complex materials while saving significant time compared to traditional methods.

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Why is it important?

We analyzed how the Li-ions move and interact with each other, calculated their movement speed, and checked how easily they can travel through the material. Our model matched well with actual experimental results, showing that Li-ions mainly move by hopping from one spot to another within the material layers. The electrical properties we calculated, like ionic conductivity and energy needed for Li-ions to move, were very close to real-life measurements.