AI agents in chemical research: GVIM – an intelligent research assistant system

Abstract

This work utilizes collected and organized instructional data from the field of chemical science to fine-tune mainstream open-source large language models. To objectively evaluate the performance of the fine-tuned models, we have developed an automated scoring system specifically for the chemistry domain, ensuring the accuracy and reliability of the evaluation results. Building on this foundation, we have designed an innovative chemical intelligent assistant system. This system employs the fine-tuned Mistral NeMo model as one of its primary models and features a mechanism for flexibly invoking various advanced models. This design fully considers the rapid iteration characteristics of large language models, ensuring that the system can continuously leverage the latest and most powerful AI capabilities. A major highlight of this system is its deep integration of professional knowledge and requirements from the chemistry field. By incorporating specialized functions such as molecular visualization, SMILES string processing, and chemical literature retrieval, the system significantly enhances its practical value in chemical research and applications. More notably, through carefully designed mechanisms for knowledge accumulation, skill acquisition, performance evaluation, and group collaboration, the system can optimize its professional abilities and interaction quality to a certain extent.