Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models
This method reduces human effort for building mid-sized materials databases, but it is incremental as it adapts existing language models to a specific domain.
The authors tackled the problem of extracting materials data from research papers by developing a flexible, model-agnostic method using general-purpose language models with human supervision, achieving up to 90% precision at 96% recall for bulk modulus data and creating a metallic glass database over twice the size of previous human-curated ones.
Accurate and comprehensive material databases extracted from research papers are crucial for materials science and engineering, but their development requires significant human effort. With large language models (LLMs) transforming the way humans interact with text, LLMs provide an opportunity to revolutionize data extraction. In this study, we demonstrate a simple and efficient method for extracting materials data from full-text research papers leveraging the capabilities of LLMs combined with human supervision. This approach is particularly suitable for mid-sized databases and requires minimal to no coding or prior knowledge about the extracted property. It offers high recall and nearly perfect precision in the resulting database. The method is easily adaptable to new and superior language models, ensuring continued utility. We show this by evaluating and comparing its performance on GPT-3 and GPT-3.5/4 (which underlie ChatGPT), as well as free alternatives such as BART and DeBERTaV3. We provide a detailed analysis of the method's performance in extracting sentences containing bulk modulus data, achieving up to 90% precision at 96% recall, depending on the amount of human effort involved. We further demonstrate the method's broader effectiveness by developing a database of critical cooling rates for metallic glasses over twice the size of previous human curated databases.