Work

Autonomous Chemistry and Materials Innovation Driven by Scientific Agents

Year: 2026

Type: article

Abstract: Large language model (LLM)-based agents are reshaping how self-driving laboratories (SDLs) may support autonomous chemical and materials research. Although SDLs have enabled major advances in mechanized experimentation and closed-loop optimization, their scientific utility remains limited when tasks require literature-grounded reasoning, adaptive coordination, and interpretation beyond predefined search spaces. In this perspective, we examine how LLM-based agents may help bridge this gap by translating scientific intent into machine-executable workflows. We propose a five-module frameworkComprehension, Design, Execution, Analysis, and Optimizationto organize the capabilities required for agent-enabled SDLs, and we discuss representative systems, including Coscientist, ChemCrow, LLM-RDF, and AI-Chemist, as milestones in this transition. We also emphasize that agent-enabled SDLs should not be conflated with autonomous scientific discovery. Safety in physical execution, hardware interoperability, reproducibility, and auditability remain central challenges. To support a more critical assessment, we introduce the HYDRA framework for benchmarking trustworthy agent-enabled workflows. Finally, we outline a human-AI-SDL collaborative model in which scientists remain responsible for scientific framing, interpretation, and oversight.

Source: JACS Au

DOI: 10.1021/jacsau.6c00213

Authors: Zikai Xie, Man Luo, Zixin Ye, Linjiang Chen, Zhuoying Zhu, Jun Jiang

Institutions: University of Science and Technology of China, University of Birmingham

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,500

Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, University of Science and Technology of China