Solid-state ion conductors hold promise as next generation battery materials.
To realize their full potential, an understanding of atomic-scale ion
conduction mechanisms is needed, including ionic and electronic degrees of
freedom. Molecular simulations can create such an understanding, Tuttavia,
including a description of electronic structure necessitates computationally
expensive methods that limit their application to small scales. We examine an
alternative approach, in which neural network models are used to efficiently
sample ionic configurations and dynamics at ab initio accuracy. Then, these
configurations are used to determine electronic properties in a post-processing
step. We demonstrate this approach by modeling the superionic phase of AgI, in
which cation diffusion is coupled to rotational motion of local electron
density on the surrounding iodide ions, termed electronic paddlewheels. The
neural network potential can capture the many-body effects of electronic
paddlewheels on ionic dynamics, but classical force field models cannot.
Through an analysis rooted the generalized Langevin equation framework, troviamo
that electronic paddlewheels have a significant impact on the time-dependent
friction experienced by a mobile cation. Our approach will enable
investigations of electronic fluctuations in materials on large length and time
scales, and ultimately the control of ion dynamics through electronic
paddlewheels.

Questo articolo esplora i giri e le loro implicazioni.

Scarica PDF: