colabfit/glass-ceramic_lithium_thiophosphate_electrolytes_

<details><summary>Cite this dataset </summary>Guo, H., and Artrith, N. glass-ceramic lithium thiophosphate electrolytes . ColabFit, 2024. https://doi.org/10.60732/0a15fe72</details>

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Dataset Name

glass-ceramic lithium thiophosphate electrolytes

Description

This database contains computationally generated atomic structures of glass-ceramics lithium thiophosphates (gc-LPS) with the general composition (Li2S)x(P2S5)1-x. Total energies and interatomic forces from density-functional theory (DFT) calculations are included. The DFT calculations used projector-augmented-wave (PAW) pseudopotentials and the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional as implemented in the Vienna Ab Initio Simulation Package (VASP) and a kinetic energy cutoff of 520 eV. The first Brillouin zone was sampled using VASP's fully automatic k-point scheme with a length parameter Rk = 25Å. The gc-LPS structures were generated using a combination of different sampling methods. Initial amorphous structure models were generated with ab initio molecular dynamics (AIMD) simulations of supercells at 1200 K using a Nose-Hoover thermostat with a time step of 1 fs. To obtain near-ground-state structures as reference for the machine-learning potential, 150 evenly spaced snapshots were extracted from the AIMD trajectories that were reoptimized with DFT geometry optimizations at zero Kelvin. Additional structures were generated by scaling the lattice parameters of the crystalline LPS structures (see below) by ±15% and perturbing atomic positions in AIMD simulations as described above.The resulting database was used to train a specialized ANN potential for the sampling of structures along the Li2S-P2S5 composition line with a genetic-algorithm (GA) as implemented in the atomistic evolution (ævo) package, following a previously reported protocol. Starting from supercells of the ideal crystal structures, either Li and S atoms were removed with a ratio of 2:1, or P and S atoms were removed with a ratio of 2:5, and low-energy configurations were determined with GA sampling. A population size of 32 trials and a mutation rate of 10% were employed. The ANN potential was iteratively refined by including additional sampled structures in the training. For each composition, at least 10 lowest energy structure models identified with the ANN-GA approach were selected and fully relaxed with DFT.Also included in the present database are the XSF files of the previously reported crystalline phases LiPS3, Li2PS3, Li4P2S7, Li7P3S11, α-Li3PS4, β-Li3PS4, γ-Li3PS4, and Li48P16S61. The crystal structures were obtained from the Inorganic Crystal Structure Database (ICSD). the Materials Project (MP) database, the Open Quantum Materials Database (OQMD), and the AFLOW database. The configuration names indicate the journal reference and the database.

Dataset authors

Haoyue Guo, Nongnuch Artrith

Publication

https://doi.org/10.1021/acs.chemmater.2c00267

Original data link

https://doi.org/10.24435/materialscloud:j5-tz

License

CC-BY-4.0

Number of unique molecular configurations

6055

Number of atoms

264604

Elements included

Li, P, S

Properties included

energy, atomic forces <br> <hr>

Usage

• —ds.parquet : Aggregated dataset information.
• —co/ directory: Configuration rows each include a structure, calculated properties, and metadata.
• —cs/ directory : Configuration sets are subsets of configurations grouped by some common characteristic. If cs/ does not exist, no configurations sets have been defined for this dataset.
• —cs_co_map/ directory : The mapping of configurations to configuration sets (if defined). <br>

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• —Dataset info schema
• —Configuration schema
• —Configuration set schema
• —Configuration set to configuration mapping schema