Discovery of oxide Li-conducting electrolytes in uncharted chemical space via topology-constrained crystal structure prediction

TL;DR

This work introduces TOPIC, a topology-constrained crystal-structure predictor designed to discover oxide Li-conducting SSEs by enforcing corner-sharing (CS) bond topologies to drastically reduce configurational complexity. By coupling TOPIC with a pretrained ML interatomic potential (SevenNet-0), the authors screen uncharted quaternary Li–O–M–X spaces, uncovering 92 CS-framework candidates and validating 15 with promising room-temperature conductivities, including Li4Hf2Si3O12 with a predicted conductivity of 14 mS cm$^{-1}$ and a band gap of 6.5 eV. A key finding is that low Li content favors CS frameworks, and the Li ratio acts as a practical descriptor for framework stability and conductivity, guiding efficient exploration of new oxide SSE prototypes. The approach provides a scalable pathway to discover high-performance oxide electrolytes in previously inaccessible chemical spaces, with implications for designing robust, thermally stable Li-ion devices.

Abstract

Oxide Li-conducting solid-state electrolytes (SSEs) offer excellent chemical and thermal stability but typically exhibit lower ionic conductivity than sulfides and chlorides. This motivates the search for new oxide materials with enhanced conductivity. Crystal structure prediction is a powerful approach for identifying such candidates. However, the structural complexity of oxide SSEs, often involving unit cells with more than 100 atoms, presents significant challenges for conventional methods. In this study, we introduce TOPIC, a structure prediction algorithm that reduces configurational complexity by enforcing corner-sharing (CS) bond topology constraints. We demonstrate that TOPIC successfully reproduces the ground-state and metastable structures of known oxide SSEs, including LiTa$_2$PO$_8$ and Li$_7$La$_3$Zr$_2$O$_{12}$, which contain up to about 200 atoms per unit cell. By combining this approach with a pretrained machine-learning interatomic potential, we systematically screen quaternary oxide compositions and identify 92 promising candidates with CS frameworks. In particular, Li$_4$Hf$_2$Si$_3$O$_{12}$, which corresponds to the ground state at its composition, exhibits an ionic conductivity of 14 mS cm$^{-1}$, a hull energy of 21 meV atom$^{-1}$, and a band gap of 6.5 eV. Through our investigation, we identify the Li ratio as one of the key factors determining the stability of CS structures. Overall, our approach provides a practical and scalable pathway for discovering high-performance oxide solid electrolytes in previously unexplored chemical spaces.

Figures (6)

• Figure 1: TOPIC algorithm. (a) Schematic illustrations of fully connected corner-sharing framework (left), framework with isolated vertex (middle), and framework with edge-sharing polyhedra (right). Dashed red circle in the middle panel indicates the isolated vertex in framework, and dashed green circles in right panel indicate nodes on edge-shared parts. Oc and T represent the cations form octahedral and tetrahedral polyhedra, respectively. (b) Overview of TOPIC algorithm. LiTiPO5 case is shown as an example. (c) Detail schematic process of cation-oxygen framework generation. Spring lines in the second panel indicate the virtual bonds between cation--oxygen pairs to which the Lennard--Jones potential is applied.
• Figure 2: Validation of the TOPIC algorithm. (a) The lowest-energy structures for each validation test system generated by TOPIC. (b) The lowest-energy and a new metastable structure in the LiTa2PO8 system obtained by TOPIC. (c) Diffusion coefficients at 400, 700, and 1000 K for all polymorphs of the LiTa2PO8 system obtained by TOPIC. Blue lines represent the known structure, the red line corresponds to the structure of the promising candidate, and gray lines indicate other polymorphs. Solid lines denote results calculated by MD simulations using SevenNet-0, while dashed lines denote results obtained by DFT simulations. (d) Revised structure generation process for cubic Li7La3Zr2O12. Left: the framework consisting of ZrO6 octahedra and LiO4 tetrahedra generated by TOPIC. Middle: the framework after La atoms are introduced via Voronoi tessellation and Monte Carlo simulation. Right: the final Li7La3Zr2O12 structure after the Li insertion.
• Figure 3: Structural stability of oxide SSEs with CS bond topology in representative element sets. (a) Upper part: Li ratio of the searched materials. Lower part: $E_{\mathrm{hull}}^{\mathrm{DFT}}$ of CS and non-CS frameworks in each composition. Compositions where no stable CS structure is found are represented by x markers. The lowest-energy atomistic structures of LiTa3P2O13, Li2ZrSiO5, Li2AlPO5, Li6Al2Si3O12, and Li5Ti2AlO8 are presented below the bar plot as example structures at each Li ratio. (b) $E_{\mathrm{hull}}^{\mathrm{DFT}}$(CS) $-$$E_{\mathrm{hull}}^{\mathrm{DFT}}$(non-CS) as a function of Li ratio. (c) Structures of CS and non-CS framework in LiTaSi2O7 and Li3AlSi2O7 and their energy above hull values (unit: meV atom$^{-1}$). (d) Ionic conductivity at 1000 K calculated by SevenNet-0 as a function of Li ratio for both CS and non-CS framework structures.
• Figure 4: Screening process. Orange boxes indicate target composition selection. Unique frameworks from these compositions are then evaluated as candidate solid electrolytes under several screening conditions (blue boxes).
• Figure 5: Final candidates. Structures are selected based on the criteria of high ionic conductivity ($\geq$0.1 mS cm$^{-1}$) and a low energy difference ($\leq$10 meV atom$^{-1}$) from the lowest-energy structure at each composition. The corresponding properties are listed in Table 2.