Frustration in Super‐Ionic Conductors Unraveled by the Density of Atomistic States

Abstract

The frustration in super‐ionic conductors enables their exceptionally high ionic conductivities, which are desired for many technological applications including batteries and fuel cells. A key challenge in the study of frustration is the difficulties in analyzing a large number of disordered atomistic configurations. Using lithium super‐ionic conductors as model systems, we propose and demonstrate the density of atomistic states (DOAS) analytics to quantitatively characterize the onset and degree of disordering, reveal the energetics of local disorder, and elucidate how the frustration enhances diffusion through the broadening and overlapping of the energy levels of atomistic states. Furthermore, material design strategies aided by the DOAS are devised and demonstrated for new super‐ionic conductors. The DOAS is generally applicable analytics for unraveling fundamental mechanisms in complex atomistic systems and guiding material design.

Crystal Structures and Phase Stability of the Li2S–P2S5 System from First Principles

“…It is possible that the soft degrees of freedom for libration of the PS 4 units as seen in several polymorphs further generate the distribution of temporary site energies which leads to low energy barrier percolation pathways . Similarly, Wang and co-workers found that adding Br into Li 3 YCl 6 to form Li 3 YBr 1.5 Cl 4.5 introduced a larger variety of closely spaced octahedral Li site energy levels, leading to a lower order–disorder transition temperature and increased Li conductivity . These previous studies highlight that engineering a more uniform Li site energy landscape can facilitate more facile Li-ion migration since there is a greater probability that neighboring sites are close in energy, which in turn can lead to lower energy barriers for Li hopping between them.…”

“…54 Similarly, Wang and co-workers found that adding Br into Li 3 YCl 6 to form Li 3 YBr 1.5 Cl 4.5 introduced a larger variety of closely spaced octahedral Li site energy levels, leading to a lower order− disorder transition temperature and increased Li conductivity. 55 These previous studies highlight that engineering a more uniform Li site energy landscape can facilitate more facile Li-ion migration since there is a greater probability that neighboring sites are close in energy, which in turn can lead to lower energy barriers for Li hopping between them. This is not a sufficient condition for high conductivity, as the component of the migration energy that is independent of the site energy also needs to be small.…”

Design of multicomponent argyrodite based on a mixed oxidation state as promising solid-state electrolyte using moment tensor potentials

“…Currently, the most promising method would be to develop and use machine-learned potentials. Recently, various machine-learning potentials, such as neural network potential (NNP), 36 deep potential (DP), 37,38 Gaussian approximation potential (GAP), 39 spectral neighbor analysis potential (SNAP), 40 and moment tensor potential (MTP) 41 have been reported. We chose the MTP among various machine learning potentials because, in terms of both performance and computational cost, it is acknowledged as the most reasonable method.…”

Insights into the local structure evolution and thermophysical properties of NaCl–KCl–MgCl2–LaCl3 melt driven by machine learning

“…The DPMD method is a significant milestone in atomistic simulation for materials because it has demonstrated high accuracy similar to that of DFT and competitive efficiency with CMD for different systems. 33–36 Successful applications of DPMD on molten salt simulations began in 2020, focusing on ZnCl 2 and its mixtures. In their study, Pan et al conducted a comparison of the partial radial distribution functions, partial structure factors, and coordination numbers using the DPMD and PIM potentials.…”