metadata
license: mit
task_categories:
- other
tags:
- materials-science
- chemistry
- ai-for-science
LLEMABench
LLEMABench is a benchmark suite for materials discovery tasks, introduced in the paper LLEMA: Evolutionary Search with LLMs for Multi-Objective Materials Discovery.
The benchmark consists of 14 realistic tasks across various domains, including:
- Electronics
- Energy
- Coatings
- Optics
- Aerospace
It evaluates candidates based on chemical plausibility, thermodynamic stability, and multi-objective property alignment (e.g., bandgap, formation energy, and bulk modulus).
Links
- GitHub Repository: https://github.com/scientific-discovery/LLEMA
- Paper: https://huggingface.co/papers/2510.22503
Evaluation and Usage
The official implementation provides scripts to evaluate CIF files for validity (property constraints) and thermodynamic stability.
Validity Analysis
To evaluate CIF files against task-specific property constraints:
python calculate_validity.py --tasks "Hard, Stiff Ceramics"
Stability Analysis
To analyze the thermodynamic stability of candidates from validity analysis results:
python calculate_stability.py --task <task_name>
Citation
@inproceedings{abhyankar2026llema,
title={LLEMA: Accelerating Materials Design via {LLM}-Guided Evolutionary Search},
author={Abhyankar, Nikhil and Kabra, Sanchit and Desai, Saaketh and Reddy, Chandan K},
booktitle={The Fourteenth International Conference on Learning Representations (ICLR)},
year={2026},
url={https://openreview.net/forum?id=TIqzhBvCNB}
}