Multi-agent systems for chemical engineering: A review and perspective

TL;DR

This paper reviews the emerging use of multi-agent systems powered by large language models in chemical engineering, highlighting recent advances, challenges, and future opportunities for transforming workflows.

Contribution

It provides a comprehensive survey of current MAS applications in chemical engineering and discusses key scientific challenges and future research directions.

Findings

Early studies show promising results in MAS applications.

Key challenges include architecture design and data integration.

Opportunities exist for rethinking chemical engineering workflows.

Abstract

Large language model (LLM)-based multi-agent systems (MASs) are a recent but rapidly evolving technology with the potential to transform chemical engineering by decomposing complex workflows into teams of collaborative agents with specialized knowledge and tools. This review surveys the state-of-the-art of MAS within chemical engineering. While early studies demonstrate promising results, scientific challenges remain, including the design of tailored architectures, integration of heterogeneous data modalities, development of foundation models with domain-specific modalities, and strategies for ensuring transparency, safety, and environmental impact. As a young but fast-moving field, MASs offer exciting opportunities to rethink chemical engineering workflows.