Data for: "Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory"

# Data for: Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory

This dataset contains the raw data to reproduce the paper:

D. Schwalbe-Koda, S. Hamel, B. Sadigh, F. Zhou, V. Lordi. "Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory". arXiv:2404.12367 (2024). DOI: [10.48550/arXiv.2404.12367](https://doi.org/10.48550/arXiv.2404.12367)

The raw data in `2025-quests-data.tar.gz` contains all the raw data to reproduce the paper.The tarfile is sorted by section of the paper (01 through 05) and supplementary information (A01 through A11).
Its structure is the following:

```
data/
├── 02-Aluminum
├── 02-GAP20
├── 02-rMD17
├── 04-TM23
├── 05-Cu
├── 05-Ta
├── A08-Denoiser
├── A11-Cu
├── A11-QTB
└── A11-Sn
```
The tarfile contains files of the following formats:- CSV files containing tables with the data for the analysis- JSON files containing structured data for the analysis- logfiles from LAMMPS simulations- Extended XYZ files containing the results of MD trajectories or materials structure data

### Citing

If you use QUESTS or its data/examples in a publication, please cite the following paper:

```bibtex
@article{schwalbekoda2024information,
title = {Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory},
author = {Schwalbe-Koda, Daniel and Hamel, Sebastien and Sadigh, Babak and Zhou, Fei and Lordi, Vincenzo},
year = {2024},
journal = {arXiv:2404.12367},
url = {https://arxiv.org/abs/2404.12367},
doi = {10.48550/arXiv.2404.12367},
}
```

# 数据集对应论文：基于信息论的原子机器学习完备性、不确定性与异常值无模型估计（Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory）

本数据集包含复现以下论文所需的原始数据：
D. Schwalbe-Koda、S. Hamel、B. Sadigh、F. Zhou、V. Lordi. 《基于信息论的原子机器学习完备性、不确定性与异常值无模型估计》. arXiv:2404.12367 (2024). DOI: [10.48550/arXiv.2404.12367](https://doi.org/10.48550/arXiv.2404.12367)

`2025-quests-data.tar.gz` 中的原始数据包含复现该论文所需的全部原始资料。该压缩包按论文章节（01至05）与补充材料（A01至A11）进行分类整理，结构如下：

data/
├── 02-Aluminum
├── 02-GAP20
├── 02-rMD17
├── 04-TM23
├── 05-Cu
├── 05-Ta
├── A08-Denoiser
├── A11-Cu
├── A11-QTB
└── A11-Sn

该压缩包包含以下格式的文件：
- 承载分析所用表格数据的CSV文件
- 承载分析所用结构化数据的JSON文件
- LAMMPS模拟日志文件
- 扩展XYZ（Extended XYZ）格式文件，内含分子动力学（MD）轨迹结果或材料结构数据

### 引用说明
若您在学术出版物中使用QUESTS或其配套数据/示例，请引用以下论文：
bibtex
@article{schwalbekoda2024information,
title = {Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory},
author = {Schwalbe-Koda, Daniel and Hamel, Sebastien and Sadigh, Babak and Zhou, Fei and Lordi, Vincenzo},
year = {2024},
journal = {arXiv:2404.12367},
url = {https://arxiv.org/abs/2404.12367},
doi = {10.48550/arXiv.2404.12367},
}