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Abstract <p>We present a domain-tailored verification framework for evaluating the scientific quality of AI-generated synthesis protocols, moving beyond generic NLP benchmarks that fail to capture chemistry-specific requirements. Our approach combines two quantitative metrics: a framework score that assesses the logical coherence of the synthesis pathway, and a weighted detail score that measures the precision of reported experimental parameters.</p> Scientific Contribution <p>This work establishes a benchmark for automated protocol generation, quantifies the gap between conceptual feasibility and parametric exactness in LLM outputs. We apply carefully curated dataset of SAC as a testbed to fine tune mainstream open source LLMs. The benchmark can be generalized to material synthesis protocols.</p>

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A chemically-aware validation framework for benchmarking large language models in materials synthesis planning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月24日

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  • Aobo Zhang

摘要

Abstract

We present a domain-tailored verification framework for evaluating the scientific quality of AI-generated synthesis protocols, moving beyond generic NLP benchmarks that fail to capture chemistry-specific requirements. Our approach combines two quantitative metrics: a framework score that assesses the logical coherence of the synthesis pathway, and a weighted detail score that measures the precision of reported experimental parameters.

Scientific Contribution

This work establishes a benchmark for automated protocol generation, quantifies the gap between conceptual feasibility and parametric exactness in LLM outputs. We apply carefully curated dataset of SAC as a testbed to fine tune mainstream open source LLMs. The benchmark can be generalized to material synthesis protocols.