用大模型预测合金相图，能自动生成新相图。
aLLoyM: A large language model for alloy phase diagram prediction
- 用开源数据库构建问答对，微调Mistral模型预测相图
- 多选题任务性能显著提升，短答模型可生成新相图
- 适合材料研发人员快速探索未知合金体系
大型语言模型（LLMs）在材料科学中具有广泛应用潜力。本文提出aLLoyM，一个针对合金成分、温度及其对应相信息进行微调的专用大模型。基于开源计算相图数据库（CPDDB）和CALPHAD评估，我们构建了二元与三元相图的问答数据集，并对Mistral模型分别进行了多选题与简答题两种格式的微调。基准测试显示，微调显著提升了模型在多选题任务上的表现。此外，aLLoyM的短答模型具备仅凭组分即生成新相图的能力，展现出加速未探索材料体系发现的巨大潜力。为促进研究与应用，我们已将短答版aLLoyM及完整基准问答数据集公开发布于Hugging Face。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are general-purpose tools with wide-ranging applications, including in materials science. In this work, we introduce aLLoyM, a fine-tuned LLM specifically trained on alloy compositions, temperatures, and their corresponding phase information. To develop aLLoyM, we curated question-and-answer (Q&A) pairs for binary and ternary phase diagrams using the open-source Computational Phase Diagram Database (CPDDB) and assessments based on CALPHAD (CALculation of PHAse Diagrams). We fine-tuned Mistral, an open-source pre-trained LLM, for two distinct Q&A formats: multiple-choice and short-answer. Benchmark evaluations demonstrate that fine-tuning substantially enhances performance on multiple-choice phase diagram questions. Moreover, the short-answer model of aLLoyM exhibits the ability to generate novel phase diagrams from its components alone, underscoring its potential to accelerate the discovery of previously unexplored materials systems. To promote further research and adoption, we have publicly released the short-answer fine-tuned version of aLLoyM, along with the complete benchmarking Q&A dataset, on Hugging Face.
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