---
language:
- en
tags:
- physics
- materials
- science
- chemistry
- mliap
- atomistic-simulations
- solid-electrolytes
- all-solid-state-batteries
- lithium-ion-batteries
pretty_name: LMX Dataset
size_categories:
- 10K<n<500K
license: cc-by-4.0
---
# LMX_Dataset

**Pretty name:** LMX Dataset  
**Tags:** materials-science, atomistic-simulations, extxyz, ase, mliap  
**License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)  
**Dataset type:** splits (train/val/test)
**Associated manuscript:** *A Database for Design and Optimization of Halide Based Solid Electrolytes for Lithium Ion Batteries* — Chiku Parida\*, Arghya Bhowmik, and Juan Maria García Lastra\*

## Summary
This dataset provided in `.extxyz` format for training and benchmarking machine-learning interatomic potentials (MLIAPs).

| Subset | Chemistry | Description |
|---|---|---|
| `dataset_aliovalent.extxyz` | LMX + aliovalent dopants | Tetravalent cation (e.g. Zr⁴⁺) substitution on the trivalent M site, creating Li vacancies and modifying ionic conductivity |
| `dataset_aliovalent_antisite.extxyz` | LMX + aliovalent dopants + antisite defects | Aliovalent-doped structures with additional antisite disorder on the cation sublattice |
| `dataset_isovalent.extxyz` | LMX + isovalent substitutions | Same-valence cation replacements (M³⁺ → M′³⁺) that tune lattice chemistry without altering Li stoichiometry |

All frames carry DFT-level **energies**, **forces**, and **stress tensors** under consistent VASP/PBE settings, making this dataset directly suitable for fitting modern MLIAP architectures (e.g. MACE, NequIP, CHGNet, ALIGNN-FF).

- **Format:** ASE-compatible EXTXYZ with keys `REF_energy`, `REF_forces`, `REF_stress`
- **Units:** energy (eV), forces (eV/Å), stress (eV/ų)
- **Total frames:** ~400,000
- **Systems:** Ternary and quaternary Li-metal-halide solid electrolytes (LiₐMXᵦ families; M = trivalent / tetravalent metals; X = Cl, Br, I)
- **DFT code / functional:** VASP / PBE

## Files
```
data/
├── dataset_aliovalent_antisite.extxyz   # aliovalent-doped + antisite-defect structures
├── dataset_aliovalent.extxyz            # aliovalent-doped structures
├── dataset_isovalent.extxyz             # isovalent-substituted structures
├── train_10k.extxyz                         # training split  (sampled 10k)
├── idd_test_2k.extxyz                          
└── ood_test_3k.extxyz                         
index.json                              
```

### Schema (per-frame)
Each frame in every `.extxyz` file exposes the following properties:

| Key | Location | Shape | Unit | Description |
|---|---|---|---|---|
| `REF_energy` | `atoms.info` | scalar | eV | Total DFT potential energy of the cell |
| `REF_forces` | `atoms.arrays` | (N, 3) | eV/Å | Atomic forces on all N atoms |
| `REF_stress` | `atoms.info` | (6,) | eV/ų | Cell stress tensor in **Voigt order** |

**Standard ASE atom properties** (positions, species, cell, periodic boundary conditions and other metadatas in atoms info) are also stored in the usual EXTXYZ fields.

## Quickstart

### Python: Download entire repository
```python
from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="cparidaAI/LMX_dataset",
    repo_type="dataset",
    local_dir="LMX_dataset",
    local_dir_use_symlinks=False,
)
print(f"Dataset downloaded to: {local_dir}")
```

### Python: Load a split with ASE
```python
from ase.io import read

# Load training split (all frames)
train = read("LMX_dataset/data/train.extxyz", index=":")
print(f"Training frames: {len(train)}")

# Access DFT labels for the first frame
frame = train[0]
energy = frame.info["REF_energy"]       # eV
forces = frame.arrays["REF_forces"]     # (N, 3) eV/Å
stress = frame.info["REF_stress"]       # (6,) eV/ų  — Voigt
```

### Python: Fetch a single file
```python
from huggingface_hub import hf_hub_download
from ase.io import read

path = hf_hub_download(
    repo_id="cparidaAI/LMX_dataset",
    repo_type="dataset",
    filename="data/ood_test_3k.extxyz",
)
test = read(path, index=":")
print(f"Test frames: {len(test)}")
```

## Benchmarks / Intended use
**Primary task:** Supervised training and benchmarking of machine-learning interatomic potentials (MLIAPs) for Li-metal-halide solid electrolytes.
**Recommended evaluation metrics:**

| Property | Metric | Units |
|---|---|---|
| Energy | MAE per atom | meV/atom |
| Forces | MAE per component | meV/Å |
| Stress | MAE per component | GPa or eV/ų |

**Downstream applications:**
- Long-timescale MD simulations of Li⁺ diffusion in halide electrolytes
- Screening of aliovalent and isovalent substitution effects on ionic conductivity
- Calculation of activation energies and diffusion coefficients at finite temperature
- Foundation for active-learning workflows to extend the chemical space of LMX electrolytes


## Provenance & generation
| Parameter | Value |
|---|---|
| Code | VASP (Vienna Ab initio Simulation Package) |
| Exchange–correlation functional | PBE (Perdew–Burke–Ernzerhof) |
| Plane-wave energy cutoff (ENCUT) | 650 eV |
| k-point sampling | Γ-point only (supercell calculations) |
| Energy convergence (EDIFF) | *(to be detailed in companion manuscript)* |
| Force convergence (EDIFFG) | *(to be detailed in companion manuscript)* |

> Full INCAR and data generation code will be found in GitHub: https://github.com/chiku-parida/LMX_data

## License
This dataset is released under the **[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)** license.

## How to cite
If you use this dataset in your work, please cite:

**Dataset manuscript (under review):**
> Chiku Parida, Arghya Bhowmik, and Juan Maria García Lastra.
> *A Database for Design and Optimization of Halide Based Solid Electrolytes for Lithium Ion Batteries.*


**This HuggingFace repository:**
> Parida, C. (2025). *LMX Dataset* [Data set]. Hugging Face.  
> https://huggingface.co/datasets/cparidaAI/LMX_dataset

**BibTeX (update DOI upon publication):**
```bibtex
@dataset{parida2026lmx,
  author    = {Parida, Chiku and Bhowmik, Arghya and {Garc\'ia Lastra}, Juan Maria},
  title     = {{A Database for Design and Optimization of Halide Based Solid Electrolytes for Lithium Ion Batteries}},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/cparidaAI/LMX_dataset},
  note      = {}
}
```

## Changelog
- v1.0.0 — initial release.

---
**Contact:** Chiku Parida, DTU Energy,