DeepSeek‐Lattice‐KG: A Compact Language Model With Knowledge Graph Augmentation for Lattice Structure Design

ABSTRACT Lattice structures enable tailored mechanical properties for aerospace, biomedical, and energy applications, yet navigating their vast design space while meeting complex performance requirements remains challenging. Here, we introduce DeepSeek‐Lattice‐KG, an intelligent framework synergistically integrating a domain‐adapted 14B‐parameter large language models with knowledge graphs for lattice structure design. The model was fine‐tuned on 2500 peer‐reviewed articles and integrated with a knowledge graph containing 658,623 entities from 50,000 publications, enabling both generative reasoning and graph‐based knowledge validation. Evaluation on 2100 expert‐curated questions across six technical domains demonstrates 94.8% accuracy, surpassing DeepSeek‐R1‐670B (88.2%) and conventional fine‐tuning approaches, while ensuring data privacy and computational efficiency. Through dynamic knowledge augmentation, the framework maintains 93% accuracy on 2025 emerging topics versus 77% for static systems, enabling real‐time updates without retraining. Engineering case studies demonstrate the framework's capability to synthesize knowledge across thousands of publications for design exploration. This work establishes a paradigm that domain‐specialized small models combined with structured knowledge provide an efficient alternative to large general‐purpose systems for specialized engineering domains.