How Correlated are Ion Hoppings in Inorganic Solid-State Electrolytes?

Despite playing a central role in the design of novel solid-state electrolytes (SSE), little is known about the processes governing ionic diffusion in these materials and the spatio-temporal correlations acting on migrating particles. However, molecular dynamics (MD) simulations can reproduce the trajectories of individual diffusing ions with extraordinary accuracy, thus providing incredibly valuable atomistic data that in practice cannot be resolved by experiments.<br/><br/>The identification of hopping events from these simulations typically relies on active supervision and definition of arbitrary material-dependent geometrical parameters, thus frustrating high throughput screenings of diffusing paths and mechanisms across simulation databases and the assessment of many-diffusing-ion correlations.<br/><br/>Here, we introduce a novel approach for analyzing ion hopping events in MD simulations in a facile and totally unsupervised manner, that allows to determine key atomistic ionic diffusion descriptors. In particular, our approach identifies with precision which and when particles diffuse in a simulation and the exact migrating paths that they follow as well.<br/><br/>We apply such a powerful analysis tool to a comprehensive database of density functional theory ab initio MD (DFT-AIMD) simulations [1,2], comprising several families of SSE and tens of millions of atomic configurations. By doing this, we are able to (1) quantify correlations between many diffusing ions, (2) identify predominant collective migrating mechanisms and (3) determine how specific and novel migration descriptors such as the length and duration of individual ionic hops correlate with ionic diffusion coefficients, all under realistic finite-temperature conditions.<br/><br/>We show that the probability for N-ions to concertedly diffuse decreases exponentially with N, and that such many-ion correlations practically do not depend on temperature. Interestingly, it is found that despite N = 2 correlations are largest, higher order many ion (N &gt; 2) coordination is more frequent in concerted diffusion. The explained unsupervised analysis approach has been implemented in the IonDiff software [3], a python code that is publicly available.<br/><br/>[1] C. López, A. Emperador, E. Saucedo, et al., Universal ion-transport descriptors and classes of inorganic solid-state electrolytes, Materials Horizons, 2023, doi: 10.1039/D2MH01516A<br/>[2] C. López, A. Emperador, E. Saucedo, et al., DFT-AIMD database, 2023, url: https://superionic.upc.edu<br/>[3] C. López, R. Rurali, C. Cazorla, Repository with all the developed codes, 2023, url: https://github.com/IonRepo/IonDiff