QCBench: Evaluating Large Language Models on Domain-Specific Quantitative Chemistry

• URL: http://arxiv.org/abs/2508.01670v1
• Date: Sun, 03 Aug 2025 08:55:42 GMT
• Title: QCBench: Evaluating Large Language Models on Domain-Specific Quantitative Chemistry
• Authors: Jiaqing Xie, Weida Wang, Ben Gao, Zhuo Yang, Haiyuan Wan, Shufei Zhang, Tianfan Fu, Yuqiang Li,
• Abstract summary: QCBench is a benchmark comprising 350 computational chemistry problems across 7 chemistry subfields.<n>Each problem focuses on pure calculations rooted in real-world chemical vertical fields.<n> Evaluations on 19 LLMs demonstrate a consistent performance degradation with increasing task complexity.
• Score: 12.18966912295507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantitative chemistry plays a fundamental role in chemistry research, enabling precise predictions of molecular properties, reaction outcomes, and material behaviors. While large language models (LLMs) have shown promise in chemistry-related tasks, their ability to perform rigorous, step-by-step quantitative reasoning remains underexplored. To fill this blank, we propose QCBench, a Quantitative Chemistry benchmark comprising 350 computational chemistry problems across 7 chemistry subfields (analytical chemistry, bio/organic chemistry, general chemistry, inorganic chemistry, physical chemistry, polymer chemistry and quantum chemistry), categorized into three hierarchical tiers-basic, intermediate, and expert-to systematically evaluate the mathematical reasoning abilities of large language models (LLMs). Designed to minimize shortcuts and emphasize stepwise numerical reasoning, each problem focuses on pure calculations rooted in real-world chemical vertical fields. QCBench enables fine-grained diagnosis of computational weaknesses, reveals model-specific limitations across difficulty levels, and lays the groundwork for future improvements such as domain adaptive fine-tuning or multi-modal integration. Evaluations on 19 LLMs demonstrate a consistent performance degradation with increasing task complexity, highlighting the current gap between language fluency and scientific computation accuracy.

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