  This paper presents a study on the integration of domain-specific knowledge
in prompt engineering to enhance the performance of large language models
(LLMs) in scientific domains. A benchmark dataset is curated to encapsulate the
intricate physical-chemical properties of small molecules, their drugability
for pharmacology, alongside the functional attributes of enzymes and crystal
materials, underscoring the relevance and applicability across biological and
chemical domains.The proposed domain-knowledge embedded prompt engineering
method outperforms traditional prompt engineering strategies on various
metrics, including capability, accuracy, F1 score, and hallucination drop. The
effectiveness of the method is demonstrated through case studies on complex
materials including the MacMillan catalyst, paclitaxel, and lithium cobalt
oxide. The results suggest that domain-knowledge prompts can guide LLMs to
generate more accurate and relevant responses, highlighting the potential of
LLMs as powerful tools for scientific discovery and innovation when equipped
with domain-specific prompts. The study also discusses limitations and future
directions for domain-specific prompt engineering development.
