---
license: mit
task_categories:
- question-answering
language:
- en
tags:
- agent
- materials_science
- spatial_reasoning
- action
pretty_name: AtomMotor
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: "train/*.json"
  - split: bench
    path: "bench/*.json"
---

# AtomWorldBench

AtomWorldBench is a benchmark and dataset for evaluating the ability of Large Language Models (LLMs) and agents to perform **3D crystal structure manipulation** from natural language instructions.

Given an input crystal structure in CIF format and a textual instruction, the model must generate the resulting crystal structure after applying the requested modification.

The dataset is released alongside the AtomWorld benchmark framework and is intended for:

- Benchmarking spatial reasoning abilities of LLMs
- Training structure-editing agents
- Supervised fine-tuning (SFT)
- Reinforcement learning and reward modeling research
- Materials-science agent evaluation

## Task Description

Each example contains:

```json
{
  "action_prompt": "...",
  "input": "...",
  "output": "...",
  // ... other metadata
}
```

In addition, the repository provides a shared `system_prompt.txt` that can be used for every task.

where:

| Field | Description |
|---------|-------------|
| `action_prompt` | Natural-language instruction describing the required structure modification |
| `input` | Input crystal structure in CIF format |
| `output` | Ground-truth crystal structure after applying the instruction |

The task is:

> Given (`action_prompt`, `input`), generate `output`.


The `bench_data` folder contains the data used in the AtomWorld Bench. Besides, we have generated ~5K data for each action, which can be used as training set.


## Evaluation

AtomWorld uses structure-aware evaluation rather than text matching.

Typical verification steps include:

1. CIF parsing
2. Atom-count verification
3. Structure matching
4. RMSD calculation

For official evaluation and benchmarking tools, see the AtomWorld repository:

https://github.com/MasterAI-EAM/atomworld

Github Page:

https://masterai-eam.github.io/atomworld/

## Repository Relationship

This Hugging Face repository contains the released datasets only.

The GitHub repository provides:

- evaluation code
- benchmark runner
- dataset generation pipeline
- API server for agent benchmarking
- visualization and analysis utilities

## Limitations

The dataset focuses on crystal-structure manipulation and does not directly evaluate:

- materials-property prediction
- electronic structure reasoning
- synthesis planning
- reaction prediction

Performance on AtomWorldBench should therefore be interpreted as a measure of structure-editing and spatial reasoning ability rather than general materials-science expertise.

## Citation

If you use AtomWorldBench in your work, please cite:

```bibtex
@misc{lv2025atomworldbenchmarkevaluatingspatial,
      title={AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Crystalline Materials},
      author={Taoyuze Lv and Alexander Chen and Fengyu Xie and Chu Wu and Jeffrey Meng and Dongzhan Zhou and Bram Hoex and Zhicheng Zhong and Tong Xie},
      year={2025},
      eprint={2510.04704},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci}
}