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We present a method for large-scale DFT-based screening of ion diffusion in crystalline solids. This is accomplished by extending the Ionic TuTraSt method to sample the potential energy surface by using single-point DFT calculations. To drastically reduce the number of single-point DFT calculations, symmetry, interpolation, and exclusion of high-energy regions are employed. This approach is tested on a large data set of solid-state Li-ion conductors, for which the interpolation and high-energy exclusion are optimized to balance computational efficiency and accuracy of the obtained diffusion properties. Furthermore, the developed workflow is validated by comparison with ab initio molecular dynamics (AIMD) simulations on a set of known Li-ion superconducting materials.

The Python package pylimer-tools is a comprehensive toolkit for computational studies of polymer networks, particularly bead-spring networks. The package provides functionality to generate polymer networks using Monte Carlo procedures and analyze their structural and mechanical properties. Key features include detection of loops, reduction of the network to its ground state energy both with and without entanglements by the Force Balance procedure, and thereafter computing the soluble and dangling fractions of network strands, as well as the equilibrium shear modulus. The toolkit supports analysis of structures generated both internally and by external simulation software such as LAMMPS. The package implements theoretical frameworks including Miller-Macosko theory and provides a dissipative particle dynamics simulator with slip-spring entanglement modeling. Built with C++ for performance and exposed through Python bindings, pylimer-tools addresses the need for specialized tools in computational polymer science.

This paper explores the impact of Mn doping in the battery electrode material Li(Mn)FePO4 by computational methods. Structures are generated at up to 6.25% Li and 3.125% Mn content with varying Li, Fe, and Mn configurations. The oxidation state of the Fe and Mn ions are explicitly set using occupation matrix control, while the structures are optimized using DFT + U. Although Mn is redox active and can exist in both the +III and +II states, there is a strong preference for the latter. We show that the charge-compensating electrons prefer to occupy the Mn sites rather than the Fe sites during Li insertion. This results in Mn(II) and Fe(II) ions that interact electrostatically with the positively charged Li ions. The DFT + U data are consequently used to parametrize a Coulomb potential, incorporating short-range two-body corrections, and utilized within a Monte Carlo approach. The short-range order between the atomic species are then quantified by their coordination numbers at varying temperatures. The Monte Carlo sampling demonstrates lower Li-Li clustering in the Li(Mn)FePO4 system at temperatures below 500 K and thus more disorder as compared to the undoped system. On average, this results in less cluster formation, which correlates with the enlarged solid solution region found in the LiMnFePO4 phase diagram.

The structure and dynamics of a material are essentially determined by the complex combination of potential energy landscapes experienced by the individual atoms in the system. In turn, valuable information on the properties of the material is encoded in the shapes of the potential energy landscape. For example, configurations of particles within a solid are determined by the shapes and presence of energetic basins, and the self-diffusion of mobile particles is defined by the geometry of how these energetic basins are connected to form paths. Understanding diffusion processes in solids at the atomistic scale is crucial for many important applications such as predicting Li-ion conduction through a solid-state battery cell or membranes for separation processes including carbon capture and water purification. While modeling can facilitate such understanding, there are still many challenges to overcome in terms of reaching relevant length and time scales that capture the complexity of the material. In this Perspective, we will discuss state-of-the-art modeling methods for mass transport inside a solid-state material and how they relate to the geometry of the potential energy landscape. We believe that approaching diffusion from a geometrical standpoint offers great promise in advancing modeling methodologies while yielding a better understanding of the structure-dynamic properties relationship and rate-limiting processes.

The Prussian blue analogues (PBAs) Na_2–xFe[Fe(CN)₆]·zH₂O (x,z = 0-2) exhibit many phase transitions as a function of the sodium and water content, which involves large volume changes that can negatively affect its energy storage performance in a battery. However, the presence of water helps stabilize the PBA framework and thus diminishes these volume changes. To improve the material for its desired applications, a deeper fundamental understanding of the interactions between water, sodium, and the PBA framework is needed. Here, the local structure and vibrational dynamics of water were studied using inelastic neutron scattering (INS), neutron diffraction, and theoretical calculations. When the sodium content is high, the material exhibits well-defined water environments that become less defined when the sodium content is lower. It was shown that the positions of sodium and water are more complex than suggested by previous diffraction and computational studies. Most of the water in the high sodium sample occupies the center of the PBA subcube, while only a small fraction is located close to the window site of the subcube. For the low sodium sample, the results suggest that a large distribution of local water environments is present. These results lay the groundwork for unraveling the ionic transport in PBAs and the development of improved energy storage materials.