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

TL;DR

This paper introduces a large dataset of synthesis recipes, a benchmark for materials synthesis tasks, and an LLM-based evaluation framework to automate and improve materials discovery processes.

Contribution

It provides a comprehensive dataset, a new benchmark, and an LLM-based evaluation method to advance automated materials synthesis prediction.

Findings

The dataset contains 17,000 expert-verified recipes.

The benchmark supports multiple synthesis prediction tasks.

The LLM-as-a-Judge aligns well with expert assessments.

Abstract

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.