Published: 2025

Leveraging Prompt Engineering in Large Language Models for Accelerating Chemical Research

CATEGORIES

RISK-BASED PROCESS SAFETY ELEMENTS

Research Summary

This outlook paper provides a comprehensive tutorial on prompt engineering techniques — zero-shot, few-shot, chain-of-thought, ReAct, RAG, and meta-prompting — applied to chemical and materials research. The authors demonstrate how each technique addresses LLM hallucination risks exacerbated by limited chemical datasets and complex technical reports, with examples in synthesis prediction and autonomous experiments. Relevant to PSM because process safety engineers face similar challenges: domain-specific terminology, complex technical documentation, and the critical need to avoid hallucinated outputs. The prompt engineering taxonomy and best practices are directly applicable to PSM tasks such as extracting information from Safety Data Sheets, interpreting process chemistry hazards, and querying chemical compatibility databases.

AUTHORS

F. Luo, J. Zhang, Q. Wang, and C. Yang

CITATIONS

F. Luo, J. Zhang, Q. Wang, and C. Yang, "Leveraging prompt engineering in large language models for accelerating chemical research," ACS Cent. Sci., vol. 11, no. 4, pp. 511-519, Apr. 2025, doi: 10.1021/acscentsci.4c01935.

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