Datasets:
File size: 3,336 Bytes
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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}
} |