license: other
license_name: noncommercial-nonprofit
tags:
- chemistry
- materials-science
- machine-learning
- computational-chemistry
- dft
- batteries
AQVolt26: High-Temperature r2SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries
Please see our blog and paper for more details about the impact of the models and dataset.
1. Dataset Details
The Parquet file can be loaded directly with the datasets library for fast browsing and filtering. Each file contains the following columns
| Column Name | Data Type | Description | Example |
|---|---|---|---|
aqvolt_id |
string | unique ID for this dataset | aqvolt_8228 |
elements |
list | unique elements that constitute the chemical system | ['I', 'Li', 'Ag'] |
composition |
dict | composition of the system with element names and counts | {"Li": 1, "Ag": 1, "I": 2} |
nsites |
int | number of atoms/sites in the system | 4 |
fmax |
float | maximum force magnitude on any single atom in eV/Å | 124.672239 |
coh_epa |
float | cohesive energy per atom in eV/atom | -3.204444 |
energy |
float | raw total energy of the system from DFT in eV | -78.693561 |
forces |
list | force on each atom from DFT in eV/Å | [[27.392877,46.40259,-112.425963],[-4.162302,-6.632746,22.376029],[-7.205259,-26.463429,29.300288],[-16.025316,-13.306415,60.749647]] |
stress |
list | stress on the the system in Voigt notation from DFT in eV/Å3 | [-3.4061113623,-4.1051214103,-0.2551386621,0.1047948581,-1.1760257427,-0.6242689209] |
magmoms |
list | magnetic moments for each atom from DFT in µB | [-3.4061113623,-4.1051214103,-0.2551386621,0.1047948581,-1.1760257427,-0.6242689209] |
atoms |
dict | ASE atoms object in dictionary form | {"numbers":[3,47,53,53], "positions":[[0.80014419,2.29470219,4.40805324],[0.66855576,3.13222298,7.18433809],[0.988119,2.65086925,3.43464628],[0.71064203,4.11512436,5.0242587]], "cell":[[1.350821,1.615872,0.485419],[0,3.321595,0.485419],[0,0,6.916288]], "pbc":[true,true,true]} |
frame_id |
float | nth frame from ab initio molecular dynamics (MD) or machine learning MD | 391 |
temperature |
float | temperature at which the configuration was generated | 900 |
metadata |
dict | additional information about the simulation conditions and structure origins | {"provenance": "materials_project", "halide_substitution": "F-I", "material_id": "mp-752767", "aimd": false, "sampled": true, "stuffed": false} |
2. Dataset Usage Guide
This guide outlines the recommended steps to access a subset of the AQVolt26 dataset.
2.1 Initial Setup
Before you begin, you need to install the necessary libraries and authenticate with Hugging Face. This is a one-time setup.
pip install datasets pandas jupyterlab ase --no-input
1. Create a Hugging Face Account: If you don't have one, create an account at huggingface.co.
2. Create an Access Token:
Navigate to your Settings -> Access Tokens page or click here. Create a new token with at least read permissions. Copy this token to your clipboard.
3. Access via Jupyter Lab:
Open your terminal or command prompt and type
python -m ipykernel install --user --name=aqvolt --display-name="aqvolt"
jupyter lab
This will open a tab in your web browser. Open a new Jupyter notebook. Make sure that your notebook is using the Python environment where the packages installed above are located when selecting your kernel. To access the dataset, type the following into a cell
from datasets import load_dataset
dataset = load_dataset("SandboxAQ/aqvolt26-dataset", split="test", token=<YOUR-TOKEN>)
filtered_dataset = dataset.filter(lambda x: "Cl" in x["elements"])
print(f"Found {len(filtered_dataset)} matching rows.")
155
from ase import Atoms
atoms_obj = Atoms(**filtered_dataset[0]['atoms'])
print(atoms_obj.get_volume())
282.5277092131659