Exploring the expertise of large language models in materials science and metallurgical engineering

Abstract

The integration of artificial intelligence into various domains is rapidly increasing, with Large Language Models (LLMs) becoming more prevalent in numerous applications. This work is included in an overall project which aims to train an LLM specifically in the field of materials science. To assess the impact of this specialized training, it is essential to establish the baseline performance of existing LLMs in materials science. In this study, we evaluated 15 different LLMs using the MaScQA question answering (Q&A) benchmark. This benchmark comprises questions from the Graduate Aptitude Test in Engineering (GATE), tailored to test models' capabilities in answering questions related to materials science and metallurgical engineering. Our results indicate that closed-source LLMs, such as Claude-3.5-Sonnet and GPT-4o, perform the best with an overall accuracy of ∼84%, while open-source models, such as Llama3-70b and Phi3-14b, top at ∼56% and ∼43%, respectively. These findings provide a baseline for the raw capabilities of LLMs on Q&A tasks applied to materials science, and emphasise the substantial improvement that could be brought to open-source models via prompt engineering and fine-tuning strategies. We anticipate that this work could push the adoption of LLMs as valuable assistants in materials science, demonstrating their utilities in this specialised domain and related sub-domains.