Robust and efficient calculation of activation energy by automated path search and density functional theory

Abstract

Because inorganic solid electrolytes are one of the key components for application to all-solid-state batteries, high-ionic-conductivity materials must be developed. Therefore, we propose a method of efficiently evaluating the activation energy of ionic diffusion by calculating a potential-energy surface (PES), searching for the optimal diffusion path by an algorithm developed using dynamic programming (DP), and calculating the corresponding activation energy by the nudged elastic band (NEB) method. Taking β-Li3PS4 as an example, the activation energy of Li-ion diffusion was calculated as 0.43, 0.25, and 0.40 eV in the a-, b-, and c-axis directions, respectively, which is in good agreement with previously reported values. By comprehensively searching for the lowest energy path by PES-DP, the arbitrariness of the path selection can be eliminated, and the activation energy must only be calculated using the NEB method a few times, which greatly reduces the computational cost required for evaluating activation energy and enables the high-throughput screening of solid state electrolytes.

Software for Evaluating Long-Range Electrostatic Interactions Based on the Ewald Summation and Its Application to Electrochemical Energy Storage Materials

“…As a result, the mobile ion at the center of the starting Voronoi region should be swapped with the vacancy of the target neighbor Voronoi center, when the test ion crosses the Voronoi boundary. This treatment reproduces the vacancy swapping during the hopping conduction …”

“…In order to improve how the BV-Ewald method handles the hopping conduction mechanism, we remove near neighbor ions of the added test ion from the unit. From the schematic 36 Although for most systems it is sufficient to remove only nearest neighbor mobile ions, for systems with strong electrostatic repulsion caused by concerted migrations, such as Li 3 N, it may be necessary also to remove the next-nearest neighbor points to minimize the erroneous interaction. Since the strength of the electrostatic repulsion is not known a priori, users can choose whether to remove the next-nearest neighbors of the added test ion (see Figure S4).…”