Leveraging Prompt Engineering in Large Language Models for Accelerating Chemical Research

Artificial intelligence (AI) using large language models (LLMs) such as GPTs has revolutionized various fields. Recently, LLMs have also made inroads in chemical research even for users without expertise in coding. However, applying LLMs directly may lead to "hallucinations", where the model generates unreliable or inaccurate information and is further exacerbated by limited data set and inherent complexity of chemical reports. To counteract this, researchers have suggested prompt engineering, which can convey human ideas formatively and unambiguously to LLMs and simultaneously improve LLMs' reasoning capability. So far, prompt engineering remains underutilized in chemistry, with many chemists barely acquainted with its principle and techniques. In this Outlook, we delve into various prompt engineering techniques and illustrate relevant examples for extensive research from metal-organic frameworks and fast-charging batteries to autonomous experiments. We also elucidate the current limitations of prompt engineering with LLMs such as incomplete or biased outcomes and constraints imposed by closed-source limitations. Although LLM-assisted chemical research is still in its early stages, the application of prompt engineering will significantly enhance accuracy and reliability, thereby accelerating chemical research.