| Dataset |
Theory level |
Specs |
File |
Size |
| nebDFT2k |
DFT(PBE) |
target: migration barrier, geometry
# of samples: 1,681 |
zip (65 MB) |
0.7 GB |
| nebDFT2k_U |
DFT(PBE+U) |
target: migration barrier, geometry
# of samples: in progress |
in progress |
|
| MPLiTrj |
DFT(PBE) |
target: energy, forces, stress tensor
# of samples: 929,066 |
zip (3.8 GB) |
16 GB |
| MPLiTrj_subsample |
DFT(PBE) |
target: energy, forces, stress tensor
# of samples: 118,024 |
zip (0.5 GB) |
2.1 GB |
| BVEL13k |
BVSE |
target: 1-3D percolation barrier
# of samples: 12,807 |
zip (11 MB) |
35 MB |
| nebBVSE122k |
BVSE |
target: migration barrier
# of samples: 122,421 |
zip (0.2 GB) |
0.9 GB |
## Python tools
### Installation
```
pip install litraj
```
or
```python
git clone https://github.com/AIRI-Institute/LiTraj
cd LiTraj
pip install .
```
### Download a specific dataset
```python
from litraj.data import download_dataset
download_dataset('BVEL13k', '.', unzip = True) # save to the current directory and unzip
```
### Read the downloaded dataset
```python
from litraj.data import load_data
train, val, text, index = load_data('BVEL13k', '.')
for atoms in train:
mp_id = atoms.info['material_id']
e1d = atoms.info['E_1D']
e2d = atoms.info['E_2D']
e3d = atoms.info['E_3D']
# do stuff
pass
```
For more details see [Datasets basics](notebooks/datasets.ipynb) in the [notebooks](#notebooks)
## Datasets in detail
### nebDFT2k dataset
The nebDFT2k dataset includes Li-ion migration trajectories and corresponding **migration barriers** for 1,681 vacancy hops between initial and final equilibrium sites. The trajectories are optimized using the climbing image nudged elastic band (NEB) method with DFT for force and energy calculations. Initial trajectories were generated via linear interpolation between start and end positions, followed by preconditioning using the BVSE-NEB method from the [ions](https://github.com/dembart/ions) library.
For benchmarking universal machine learning interatomic potentials (uMLIPs), the BVSE-NEB optimized initial trajectories serve as the starting point for uMLIP-NEB optimization. The resulting trajectories are compared to the DFT ground truth. See an [example](notebooks/nebDFT2k_benchmark_MACE_MP_small.ipynb) to learn how to benchmark the pre-trained [MACE_MP](https://github.com/ACEsuit/mace-mp) model.
As an input for graph neural network (GNN) models for structure-to-property prediction of Li-ion migration barriers, we use the supercells with a centroid, marked as 'X' chemical element, added between the starting and final positions of the migrating ion.
**Structure of the nebDFT2k dataset**
nebDFT2k/
├── nebDFT2k_index.csv # Table with material_id, edge_id, chemsys, _split, .., columns
├── edge-id1_init.xyz # Initial (BVSE-NEB optimized) trajectory file, edge_id1 = mp-id1_source_target_offsetx_offsety_offsetz
├── edge-id1_relaxed.xyz # Final (DFT-NEB optimized) trajectory file
├── ...
└── nebDFT2k_centroids.xyz # File with centroid supercells
**Usage example**
```python
from litraj.data import download_dataset, load_data
# download the dataset to the selected folder
download_dataset('nebDFT2k', 'path/to/save/data')
# read the dataset from the folder
data = load_data('nebDFT2k', 'path/to/save/data')
data_train = data[data._split == 'train']
for atoms in data_train.centroid:
edge_id = atoms.info['edge_id']
mp_id = atoms.info['material_id']
em = atoms.info['em']
# do stuff
for traj_init, traj_relaxed in zip(data.trajectory_init, data.trajectory_relaxed):
# do stuff
pass
```
### MPLiTrj dataset
The MPLiTrj dataset contains 929,066 configurations with calculated energies, forces and stress tensors obtained during the DFT-NEB optimization of 2,698 Li-ion migration pathways. Its subsample comprises 118,024 configurations. Each dataset has three .xyz files corresponding to the training, validation, and test data splits.
**Structure of the MPLiTrj dataset**
MPLiTraj/
├── MPLiTrj_train.xyz # Training file
├── MPLiTrj_val.xyz # Validation file
└── MPLiTrj_test.xyz # Test file
**Usage example**
```python
from litraj.data import download_dataset, load_data
# download the dataset to the selected folder
download_dataset('MPLiTrj_subsample', 'path/to/save/data')
train, val, test = load_data('MPLiTrj_subsample', 'path/to/save/data')
structures, energies, forces, stresses = [], [], [], []
for atoms in train:
structures.append(atoms)
energies.append(atoms.calc.get_potential_energy())
forces.append(atoms.calc.get_forces().tolist())
stresses.append(atoms.calc.get_stress().tolist())
```
### BVEL13k dataset
The BVEL13k dataset contains Li-ion 1-3D percolation barriers calculated for 12,807 Li-containing ionic crystal structures. The percolation barriers are calculated using BVEL method as implemented in the [BVlain](https://github.com/dembart/BVlain) python package. There are 73 chemical elements (species), each structure contains at most 160 atoms and has a unit cell volume smaller than 1500 Å