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This is a Quantitative Study study.

September 20, 2025

Mat-Instructions: A Large-Scale Inorganic Material Instruction Dataset for Large Language Models

Key Points

The introduction of Mat-Instruction significantly improves LLMs' ability to predict materials' structures and properties, fostering innovation.
Fine-tuning LLaMA with Mat-Instruction has led to extensive improvements in materials science applications, achieving notable benchmarks.
This dataset addresses the critical gap in high-quality instruction-driven datasets, establishing a foundation for future materials research.
Unlocking LLMs' potential in materials science could accelerate progress towards sustainable development goals, benefiting various industries.

Abstract

Recent advancements in large language models (LLMs) have revolutionized research discovery across various scientific disciplines, including materials science. The discovery of novel materials, particularly crystal materials, is essential for achieving sustainable development goals (SDGs), as they drive breakthroughs in climate change mitigation, clean and affordable energy, and the promotion of industrial innovation. However, unlocking the full potential of LLMs in materials research remains challenging due to the lack of high-quality, diverse, and instruction-based datasets. Such datasets are crucial for guiding these models in understanding and predicting the structure, property, and function of materials across various tasks. To address this limitation, we introduce Mat-Instruction, a large-scale inorganic material instruction dataset, specifically designed to unlock the potential of LLMs in materials science. Extensive experiments on fine-tuning LLaMA with our Mat-Instruction dataset demonstrate its effectiveness in advancing progress for materials science. The code and dataset are available at https://github.com/zjuKeLiu/Mat-Instructions

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