邁向全自動化材料發現：基於大規模合成數據集與專家級LLM評判機制

摘要

材料合成對於能源儲存、催化、電子學及生物醫學設備等領域的創新至關重要。然而，這一過程主要依賴於基於專家直覺的經驗性試錯法。我們的工作旨在通過提供一個實用的、數據驅動的資源來支持材料科學界。我們從公開文獻中精心整理了一個包含17,000條專家驗證合成配方的全面數據集，這構成了我們新開發的基準——AlchemyBench的基礎。AlchemyBench提供了一個端到端的框架，支持大語言模型應用於合成預測的研究。它涵蓋了關鍵任務，包括原材料與設備預測、合成程序生成及表徵結果預測。我們提出了一個LLM-as-a-Judge框架，利用大語言模型進行自動化評估，展示了與專家評估高度一致的統計結果。總體而言，我們的貢獻為探索大語言模型在預測和指導材料合成方面的能力提供了堅實的基礎，最終為更高效的實驗設計和加速材料科學創新鋪平了道路。

English

Materials synthesis is vital for innovations such as energy storage, catalysis, electronics, and biomedical devices. Yet, the process relies heavily on empirical, trial-and-error methods guided by expert intuition. Our work aims to support the materials science community by providing a practical, data-driven resource. We have curated a comprehensive dataset of 17K expert-verified synthesis recipes from open-access literature, which forms the basis of our newly developed benchmark, AlchemyBench. AlchemyBench offers an end-to-end framework that supports research in large language models applied to synthesis prediction. It encompasses key tasks, including raw materials and equipment prediction, synthesis procedure generation, and characterization outcome forecasting. We propose an LLM-as-a-Judge framework that leverages large language models for automated evaluation, demonstrating strong statistical agreement with expert assessments. Overall, our contributions offer a supportive foundation for exploring the capabilities of LLMs in predicting and guiding materials synthesis, ultimately paving the way for more efficient experimental design and accelerated innovation in materials science.

邁向全自動化材料發現：基於大規模合成數據集與專家級LLM評判機制

Towards Fully-Automated Materials Discovery via Large-Scale Synthesis Dataset and Expert-Level LLM-as-a-Judge

摘要

Summary

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