| --- |
| 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} |
| } |
