# GAIA

**Repository Path**: pfsuo/GAIA

## Basic Information

- **Project Name**: GAIA
- **Description**: GAIA from github
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-04-08
- **Last Updated**: 2026-04-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="center">
  <img src="./gaia_logo.png" alt="gaia_logo" width="250"/>
</div>

# GAIA
GAIA is a framework to generate datasets with an automated pipeline for machine learning interatomic potentials.

- [GAIA](#gaia)
  * [Prerequisites](#prerequisites)
  * [Usage](#usage)
    + [Config file](#config-file)
    + [Data-generator](#data-generator)
    + [Data-improver](#data-improver)
    + [GAIA-Bench](#benchmark)
  * [Dataset and model checkpoint](#dataset-and-model-checkpoint)
  * [Citation](#citation)

## Prerequisites

### Quantum mechanics (QM) package
A QM package is required to run GAIA. <br>
Currently it is designed to use `VASP`, but will support more packages later.

### Distributed environment with shared storage
GAIA has been implemented under the assumption of distributed environment. <br>
Also, `shared storage` is required for each node to access the same directory with an identical path.

### Job scheduler
GAIA is currently designed to use `SLURM` as the job scheduler, <br>
but with minor code modifications, one can easily adapt it to other schedulers or execute it on a single node.

### Dependencies
We provide `requirements.txt` that allows users to fully reproduce the environment used for the GAIA implementation. <br>
GAIA also requires the following binaries:
[CREST](https://github.com/crest-lab/crest), [nebmake.pl](https://theory.cm.utexas.edu/henkelman/code/), [Open Babel](https://github.com/openbabel/openbabel), [xTB](https://github.com/grimme-lab/xtb), [xTB-IFF](https://github.com/grimme-lab/xtbiff)

## Usage

### Config file
- [user_config](https://github.samsungds.net/SAIT/GAIA/tree/main/examples/config.yaml)
provides an example YAML file with user-defined settings
for the data-generator, data-improver, and GAIA-Bench.
- [base_config](https://github.samsungds.net/SAIT/GAIA/blob/main/src/commons/config_base.yaml)
serves as a skeleton configuration.
It includes default values for advanced parameters,
while user-defined parameters override those in the base config.

### Data-generator

#### Input preparation

- Chemical components <br>
  GAIA supports both periodic (e.g., metals) and non-periodic (e.g., molecules with organic species) components. <br>
  Each should follow the format of .POSCAR and .xyz, respectively.

#### Run
```
$ cd GAIA
$ python main.py -a data_generator -c {user_config (.yaml)} -o {out_dir} -p {prefix}
```
- If out_dir is `/home/GAIA_out` and prefix is `first`, artifacts and the log is saved in `/home/GAIA_out/first/`

### Data-improver

#### Input preparation

  - Trainset, validset and model checkpoint <br>
    Data improver provides recommendations based on error metrics on validset, as well as trainset itself, <br>
    which requires a valid dataset (.extxyz) and a trained model checkpoint (e.g. .pt or .pth), in addition to a train dataset. <br>
    The MLIP framework with `calculator` for the checkpoint should be also set up.
    
#### Run
```
$ cd GAIA
$ python main.py -a data_improver -c {user_config (.yaml)} -o {out_dir} -p {prefix}
```

### GAIA-Bench

#### Input preparation

  - GAIA-Bench datasets and model checkpoint <br>
    GAIA-Bench includes four benchmark tasks, of which the datasets are available at [GAIA-Bench](https://huggingface.co/datasets/aixsim/GAIA-Bench) <br>
    A model checkpoint to test is required; the MLIP framework with `calculator` for the checkpoint should be also set up.
    
#### Run
```
$ cd GAIA
$ python main.py -a benchmark -c {user_config (.yaml)} -o {out_dir} -p {prefix}
```

## Dataset and model checkpoint
[Titan25](https://huggingface.co/datasets/aixsim/Titan25) is an MLIP dataset constructed with GAIA,
comprising 1.8M data points across 11 elements.
[SNet-T25](https://huggingface.co/aixsim/SNet-T25) is an MLIP trained on this dataset.
See [GAIA paper](https://arxiv.org/abs/2509.25798) for details.

## Citation
If using this code, please cite our work as follows:
```
@article{gaia2025,
  title={Scalable Reactive Atomistic Dynamics with GAIA},
  author={Song, Suhwan and Kim, Heejae and Jang, Jaehee and Cho, Hyuntae and Kim, Gunhee and Kim, Geonu},
  journal={arXiv preprint arXiv:2509.25798},
  year={2025}
}
```