Automated electrosynthesis reaction mining with multimodal large language models (MLLMs).

ABSTRACT: Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prom

SUBMITTER: Leong SX 

PROVIDER: S-EPMC11462585 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature