AI-Powered Workflow for Constructing Organic Materials Databases from the Literature: Integrating Large Language Models.

ABSTRACT: We developed an end-to-end workflow to automate the construction of materials science databases from published literature, addressing a traditionally manual, time-intensive, and labor-intensive process. The work systematically evaluates and compares different machine learning (ML) methods to optimize each task. For identifying relevant publications, we tested various ML techniques and concluded that a combination of large language model (LLM)-based embeddings, clustering, and direct LLM queries is most effective. In the subsequent data extraction phase, we employed OpenAI's GPT-4 to extract materials and their properties, achieving accuracy comparable to manually curated data sets. Additionally, we integrated AI/ML methods to automatically generate SMILES from chemical structure images, ex

SUBMITTER: Hu H 

PROVIDER: S-EPMC12572996 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature