README.md

---
license: other
extra_gated_prompt: "The AQVolt26 models and dataset are for non-commercial use. If you want to work with us or discuss obtaining a commercial license, please contact us directly at materials@sandboxaq.com."
extra_gated_fields:
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tags:
- chemistry
- materials-science
- machine-learning
- computational-chemistry
- dft
- batteries
---


<h1 align="center" style="font-size: 36px;">
  <span style="color: #DAA520;">AQ</span>Volt26 Universal Potentials 🔋</h1>

<p align="center">
  <img src="aqvolt26.png" alt="AQVolt26 Overview" width="600">
</p>


This repository contains **AQVolt26** model checkpoints. The models released currently are based on the Smooth Energy Network (eSEN) architecture, co-trained with a combination of datasets.


Please see our [blog](https://www.sandboxaq.com/post/aqvolt26-advancing-ai-driven-discovery-for-next-generation-solid-state-batteries) and [paper](https://arxiv.org/abs/2604.02524) for more details about the impact of the models and [dataset](https://huggingface.co/collections/SandboxAQ/aqvolt26).

## 1. Model Installation

This section details how to install and run the eSEN models.

### Step 1.1: Create Environment

First, create and activate a new micromamba (or conda) environment with Python 3.10.

```bash
micromamba create -n aqvolt python=3.10
micromamba activate aqvolt
```

### Step 1.2: Install Dependencies

Install all the required libraries

```bash
pip install huggingface_hub datasets pandas jupyterlab ase fairchem-core --no-input
```

### Step 1.3: Log in to Hugging Face
If you don't have one, create an account at [huggingface.co](https://huggingface.co/join).

**Create an Access Token:**
Navigate to your **Settings -> Access Tokens** page or click [here](https://huggingface.co/settings/tokens). Create a new token with at least **`read`** permissions. Copy this token to your clipboard.

### Step 1.4: Download the AQVolt26 Models

**Access via Jupyter Lab:**

Open your terminal or command prompt and type 

```bash
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.
Type the following into a cell

```bash
import torch
from huggingface_hub import hf_hub_download
```

You can access the foundational potential more suited to off-equilibrium tasks at this checkpoint named **aqvolt-esen.pt** or the version more suited to tasks near 0K called **aqvolt-mp-esen.pt**.

```bash
model_path = hf_hub_download(
  repo_id="SandboxAQ/aqvolt26-models",
  filename="aqvolt-esen.pt", 
  token=<YOUR-TOKEN>
)
```

Load the model as an ASE-compatible calculator

```bash
from fairchem.core import FAIRChemCalculator
from fairchem.core.units.mlip_unit import load_predict_unit

predictor = load_predict_unit(path=model_path)
calc = FAIRChemCalculator(predictor)
```

## 2. Model Usage

You can test the model with a subset of the AQVolt26 test data

```bash
from datasets import load_dataset
dataset = load_dataset("SandboxAQ/aqvolt26-dataset-subset", split="test", token=<YOUR-TOKEN>)

from ase import Atoms
atoms = Atoms(**dataset[0]['atoms'])
atoms.calc = calc

print(atoms.get_potential_energy())
print(atoms.get_forces())
print(atoms.get_stress())
```

**Example: Run a Molecular Dynamics NpT simulation with an AQVolt26 model:**

Set the desired inputs

```bash
temperature = 400 # in Kelvin
pressure = 1 # in atm
timestep = 1 # in fs
interval = 1 # frequency of data logging, 1 for every frame
nsteps = 10 # MD steps to run
```

Convert the inputs into the correct units

```bash
import numpy as np
import scipy.constants as const
from ase import units

pressure_pa = pressure * const.atm  
angstrom_to_meter = const.angstrom  
joule_to_ev = 1 / const.e 
pressure_ev_per_cubic_meter = pressure_pa * joule_to_ev
conversion_factor_m3_to_A3 = angstrom_to_meter**3
pressure_ev_per_cubic_angstrom = pressure_ev_per_cubic_meter * conversion_factor_m3_to_A3
```

Scale the initial velocity

```bash
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
MaxwellBoltzmannDistribution(atoms, temperature_K=temperature)
```

Feed in the MD inputs

```bash
from ase.md.npt import NPT
dyn = NPT(
    atoms=atoms, 
    timestep=timestep * units.fs, 
    temperature_K=temperature, 
    externalstress=pressure_ev_per_cubic_angstrom, 
    ttime=float(25.0) * units.fs,
    pfactor=float(75.0**2.0) * units.fs,
    mask=np.array([(1, 0, 0), (0, 1, 0), (0, 0, 1)]) # mask for isotropic pressure
)
```

Set up a logger to monitor your run

```bash
from ase.md.logger import MDLogger
logger = MDLogger(
    dyn,
    atoms,
    'aqvolt.log',
    header=True,
    peratom=False,
    stress=False,
)
dyn.attach(logger, interval=interval)
```

Define your trajectory file to read the generated configurations

```bash
from ase.io import read, write, Trajectory
traj = Trajectory('aqvolt.traj', 'w', atoms)
dyn.attach(traj.write, interval=interval) 
```

Run the NpT simulation for the desired period of time

```bash
dyn.run(nsteps)
```

## 3. How to Cite

If you use the AQVolt26 models or dataset in your research, please cite the following paper:

```
Jiyoon Kim, Chuhong Wang, Aayush R. Singh, Tyler Sours, Shivang Agarwal, AJ Nish, Paul Abruzzo, Ang Xiao, Omar Allam. (2026). AQVolt26: High-Temperature r2SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries. arXiv preprint arXiv:XXXX.XXXXX.
```

### BibTeX Entry

```bibtex
@article{kim2026aqvolt26hightemperaturer2scanhalide,
      title={AQVolt26: High-Temperature r2SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries}, 
      author={Jiyoon Kim and Chuhong Wang and Aayush R. Singh and Tyler Sours and Shivang Agarwal and AJ Nish and Paul Abruzzo and Ang Xiao and Omar Allam},
      year={2026},
      eprint={2604.02524},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci},
      url={https://arxiv.org/abs/2604.02524}, 
}
```
---